Repository: econ-ark/HARK (language: python)

Issue:
Sequence Space Jacobian Type
Integrating @wdu9's work on SSJ. 

The results are very wrong... which is probably due to my lack of experience with SSJ

Would appreciate help from @wdu9 and @Mv77 

Sequence Space Jacobian Type
Integrating @wdu9's work on SSJ. 

The results are very wrong... which is probably due to my lack of experience with SSJ

Would appreciate help from @wdu9 and @Mv77 



Discussion / hints:
I see this in the runs:

```
FAILED HARK/ConsumptionSaving/tests/test_SequenceSpaceJacobianType.py::test_Transition_Matrix_Methods::test_calc_tran_matrix - AttributeError: 'SequenceSpaceJacobianType' object has no attribute 'taxrate'. Did you mean: 'tax_rate'?
FAILED HARK/ConsumptionSaving/tests/test_SequenceSpaceJacobianType.py::test_Jacobian_methods::test_calc_jacobian - AttributeError: 'SequenceSpaceJacobianType' object has no attribute 'taxrate'. Did you mean: 'tax_rate'?
```
A minor comment is that, since SSJ is a method and not a model, I don't think that SSJType is appropriate. Maybe NewKeynesianType?
tried to fix the errors on the notebooks, but there's still something off with the "NewKeyensianType"
I'm going to try to look at this ASAP. Alan's latest commit turns off some (probably unnecessary) pre-solve behavior. The change(s) that I made to `calc_jacobian` had interactions with `presolve`. Before my edits, the method would edit the attribute `_cFunc_terminal`, which is a *class* attribute, not instance. I also added functionality to `solve` so that the user can indicate that they *don't* want to run `presolve` as part of `solve`. That enables doing things like a custom "terminal solution", like in this case.
After my last commit, the first column of the Jacobians was correct, but the others were still off.
Unfortunately, the changes in the meantime made them all wrong again. I assume it has something to do with the changes made to `IndShockConsumerType` and the updating process...
Steady states and policy functions of the infinite horizon solution look fine and unchanged. Only the Jacobian part is off. 
That's a good hint about what's going on. To further help me hypothesize
solutions while not actually looking at the code, which variable is "the
first column"?

On Fri, May 31, 2024, 3:48 PM Adrian Monninger ***@***.***>
wrote:

> After my last commit, the first column of the Jacobians was correct, but
> the others were still off.
> Unfortunately, the changes in the meantime made them all wrong again. I
> assume it has something to do with the changes made to
> IndShockConsumerType and the updating process...
> Steady states and policy functions of the infinite horizon solution look
> fine and unchanged. Only the Jacobian part is off.
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/econ-ark/HARK/pull/1416#issuecomment-2142887617>, or
> unsubscribe
> <https://github.com/notifications/unsubscribe-auth/ADKRAFN7DQ2OWORDOPOSGUDZFDHYTAVCNFSM6AAAAABGW5R3SSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNBSHA4DONRRG4>
> .
> You are receiving this because you commented.Message ID:
> ***@***.***>
>

In the temporarily created notebook: IndShockConsumerType_HANK.ipynb
In cell 13, we solve:
CJACR, AJACR = Agent_GE.calc_jacobian("Rfree", T)
CJACN, AJACN = Agent_GE.calc_jacobian("labor", T)

The first column of CJACR should start at 0.4 and then converge to 0.

This part is created by the ZerothColAgent in the function `calc_jacobian`


I see this in the runs:

```
FAILED HARK/ConsumptionSaving/tests/test_SequenceSpaceJacobianType.py::test_Transition_Matrix_Methods::test_calc_tran_matrix - AttributeError: 'SequenceSpaceJacobianType' object has no attribute 'taxrate'. Did you mean: 'tax_rate'?
FAILED HARK/ConsumptionSaving/tests/test_SequenceSpaceJacobianType.py::test_Jacobian_methods::test_calc_jacobian - AttributeError: 'SequenceSpaceJacobianType' object has no attribute 'taxrate'. Did you mean: 'tax_rate'?
```
A minor comment is that, since SSJ is a method and not a model, I don't think that SSJType is appropriate. Maybe NewKeynesianType?
tried to fix the errors on the notebooks, but there's still something off with the "NewKeyensianType"
I'm going to try to look at this ASAP. Alan's latest commit turns off some (probably unnecessary) pre-solve behavior. The change(s) that I made to `calc_jacobian` had interactions with `presolve`. Before my edits, the method would edit the attribute `_cFunc_terminal`, which is a *class* attribute, not instance. I also added functionality to `solve` so that the user can indicate that they *don't* want to run `presolve` as part of `solve`. That enables doing things like a custom "terminal solution", like in this case.
After my last commit, the first column of the Jacobians was correct, but the others were still off.
Unfortunately, the changes in the meantime made them all wrong again. I assume it has something to do with the changes made to `IndShockConsumerType` and the updating process...
Steady states and policy functions of the infinite horizon solution look fine and unchanged. Only the Jacobian part is off. 
That's a good hint about what's going on. To further help me hypothesize
solutions while not actually looking at the code, which variable is "the
first column"?

On Fri, May 31, 2024, 3:48 PM Adrian Monninger ***@***.***>
wrote:

> After my last commit, the first column of the Jacobians was correct, but
> the others were still off.
> Unfortunately, the changes in the meantime made them all wrong again. I
> assume it has something to do with the changes made to
> IndShockConsumerType and the updating process...
> Steady states and policy functions of the infinite horizon solution look
> fine and unchanged. Only the Jacobian part is off.
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/econ-ark/HARK/pull/1416#issuecomment-2142887617>, or
> unsubscribe
> <https://github.com/notifications/unsubscribe-auth/ADKRAFN7DQ2OWORDOPOSGUDZFDHYTAVCNFSM6AAAAABGW5R3SSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNBSHA4DONRRG4>
> .
> You are receiving this because you commented.Message ID:
> ***@***.***>
>

In the temporarily created notebook: IndShockConsumerType_HANK.ipynb
In cell 13, we solve:
CJACR, AJACR = Agent_GE.calc_jacobian("Rfree", T)
CJACN, AJACN = Agent_GE.calc_jacobian("labor", T)

The first column of CJACR should start at 0.4 and then converge to 0.

This part is created by the ZerothColAgent in the function `calc_jacobian`



Fix patch:
diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
index 53e53eb8f..a9f53ac19 100644
--- a/Documentation/CHANGELOG.md
+++ b/Documentation/CHANGELOG.md
@@ -8,6 +8,18 @@ For more information on HARK, see [our Github organization](https://github.com/e
 
 ## Changes
 
+### 0.15.1 (in development)
+
+Release Date: TBD
+
+#### Major Changes
+
+none yet
+
+#### Minor Changes
+
+- Adds example of integration of HARK with SSJ toolkit. [#1447](https://github.com/econ-ark/HARK/pull/1447)
+
 ### 0.15.0
 
 Release Date: June 4, 2024
@@ -16,7 +28,7 @@ Note: Due to major changes on this release, you may need to adjust how AgentType
 
 This release drops support for Python 3.8 and 3.9, consistent with SPEC 0, and adds support for Python 3.11 and 3.12. We expect that all HARK features still work with the older versions, but they are no longer part of our testing regimen.
 
-### Major Changes
+#### Major Changes
 
 - Drop official support for Python 3.8 and 3.9, add support for 3.11 and 3.12. [#1415](https://github.com/econ-ark/HARK/pull/1415)
 - Replace object-oriented solvers with single function versions. [#1394](https://github.com/econ-ark/HARK/pull/1394)
@@ -28,7 +40,7 @@ This release drops support for Python 3.8 and 3.9, consistent with SPEC 0, and a
 - Such constructed inputs can use alternate parameterizations / formats by changing the `constructor` function and providing its arguments in `parameters`.
 - Move `HARK.datasets` to `HARK.Calibration` for better organization of data and calibration tools. [#1430](https://github.com/econ-ark/HARK/pull/1430)
 
-### Minor Changes
+#### Minor Changes
 
 - Add option to pass pre-built grid to `LinearFast`. [#1388](https://github.com/econ-ark/HARK/pull/1388)
 - Moves calculation of stable points out of ConsIndShock solver, into method called by post_solve [#1349](https://github.com/econ-ark/HARK/pull/1349)
diff --git a/HARK/Calibration/Income/IncomeProcesses.py b/HARK/Calibration/Income/IncomeProcesses.py
index 82fd021e1..1221207b4 100644
--- a/HARK/Calibration/Income/IncomeProcesses.py
+++ b/HARK/Calibration/Income/IncomeProcesses.py
@@ -122,6 +122,61 @@ def __init__(self, sigma, UnempPrb, IncUnemp, n_approx, seed=0):
         super().__init__(pmv=dstn_approx.pmv, atoms=dstn_approx.atoms, seed=seed)
 
 
+class MixtureTranIncShk_HANK(DiscreteDistribution):
+    """
+    A one-period distribution for transitory income shocks that are a mixture
+    between a log-normal and a single-value unemployment shock. This version
+    has additional parameters that makes it useful for HANK models.
+
+    Parameters
+    ----------
+    sigma : float
+        Standard deviation of the log-shock.
+    UnempPrb : float
+        Probability of the "unemployment" shock.
+    IncUnemp : float
+        Income shock in the "unemployment" state.
+    n_approx : int
+        Number of points to use in the discrete approximation.
+    tax_rate : float
+        Flat tax rate on labor income.
+    labor : float
+        Intensive margin labor supply.
+    wage : float
+        Wage rate scaling factor.
+    seed : int, optional
+        Random seed. The default is 0.
+    Returns
+    -------
+    TranShkDstn : DiscreteDistribution
+        Transitory income shock distribution.
+    """
+
+    def __init__(
+        self,
+        sigma,
+        UnempPrb,
+        IncUnemp,
+        n_approx,
+        wage,
+        labor,
+        tax_rate,
+        seed=0,
+    ):
+        dstn_approx = MeanOneLogNormal(sigma).discretize(
+            n_approx if sigma > 0.0 else 1, method="equiprobable", tail_N=0
+        )
+
+        if UnempPrb > 0.0:
+            dstn_approx = add_discrete_outcome_constant_mean(
+                dstn_approx, p=UnempPrb, x=IncUnemp
+            )
+        # Rescale the transitory shock values to account for new features
+        TranShkMean_temp = (1.0 - tax_rate) * labor * wage
+        dstn_approx.atoms *= TranShkMean_temp
+        super().__init__(pmv=dstn_approx.pmv, atoms=dstn_approx.atoms, seed=seed)
+
+
 class BufferStockIncShkDstn(DiscreteDistributionLabeled):
     """
     A one-period distribution object for the joint distribution of income
@@ -178,7 +233,6 @@ def __init__(
             IncUnemp=IncUnemp,
             n_approx=n_approx_Tran,
         )
-
         joint_dstn = combine_indep_dstns(perm_dstn, tran_dstn)
 
         super().__init__(
@@ -190,6 +244,85 @@ def __init__(
         )
 
 
+class IncShkDstn_HANK(DiscreteDistributionLabeled):
+    """
+    A one-period distribution object for the joint distribution of income
+    shocks (permanent and transitory), as modeled in the Buffer Stock Theory
+    paper:
+        - Lognormal, discretized permanent income shocks.
+        - Transitory shocks that are a mixture of:
+            - A lognormal distribution in normal times.
+            - An "unemployment" shock.
+
+    This version has additional features that make it particularly useful for HANK models.
+
+    Parameters
+    ----------
+    sigma_Perm : float
+        Standard deviation of the log- permanent shock.
+    sigma_Tran : float
+        Standard deviation of the log- transitory shock.
+    n_approx_Perm : int
+        Number of points to use in the discrete approximation of the permanent shock.
+    n_approx_Tran : int
+        Number of points to use in the discrete approximation of the transitory shock.
+    UnempPrb : float
+        Probability of the "unemployment" shock.
+    IncUnemp : float
+        Income shock in the "unemployment" state.
+    tax_rate : float
+        Flat tax rate on labor income.
+    labor : float
+        Intensive margin labor supply.
+    wage : float
+        Wage rate scaling factor.
+    neutral_measure : Bool, optional
+        Whether to use Harmenberg's permanent-income-neutral measure. The default is False.
+    seed : int, optional
+        Random seed. The default is 0.
+    Returns
+    -------
+    IncShkDstn : DiscreteDistribution
+        Income shock distribution.
+    """
+
+    def __init__(
+        self,
+        sigma_Perm,
+        sigma_Tran,
+        n_approx_Perm,
+        n_approx_Tran,
+        UnempPrb,
+        IncUnemp,
+        tax_rate,
+        labor,
+        wage,
+        neutral_measure=False,
+        seed=0,
+    ):
+        perm_dstn = LognormPermIncShk(
+            sigma=sigma_Perm, n_approx=n_approx_Perm, neutral_measure=neutral_measure
+        )
+        tran_dstn = MixtureTranIncShk_HANK(
+            sigma=sigma_Tran,
+            UnempPrb=UnempPrb,
+            IncUnemp=IncUnemp,
+            n_approx=n_approx_Tran,
+            wage=wage,
+            labor=labor,
+            tax_rate=tax_rate,
+        )
+        joint_dstn = combine_indep_dstns(perm_dstn, tran_dstn)
+
+        super().__init__(
+            name="HANK",
+            var_names=["PermShk", "TranShk"],
+            pmv=joint_dstn.pmv,
+            atoms=joint_dstn.atoms,
+            seed=seed,
+        )
+
+
 ###############################################################################
 
 
@@ -307,6 +440,143 @@ def construct_lognormal_income_process_unemployment(
     return IncShkDstn
 
 
+def construct_HANK_lognormal_income_process_unemployment(
+    T_cycle,
+    PermShkStd,
+    PermShkCount,
+    TranShkStd,
+    TranShkCount,
+    T_retire,
+    UnempPrb,
+    IncUnemp,
+    UnempPrbRet,
+    IncUnempRet,
+    tax_rate,
+    labor,
+    wage,
+    RNG,
+    neutral_measure=False,
+):
+    """
+    Generates a list of discrete approximations to the income process for each
+    life period, from end of life to beginning of life.  Permanent shocks are mean
+    one lognormally distributed with standard deviation PermShkStd[t] during the
+    working life, and degenerate at 1 in the retirement period.  Transitory shocks
+    are mean one lognormally distributed with a point mass at IncUnemp with
+    probability UnempPrb while working; they are mean one with a point mass at
+    IncUnempRet with probability UnempPrbRet.  Retirement occurs after t=T_retire
+    periods of working.
+
+    This version of the function incorporates additional flexibility with respect
+    to transitory income (continuous labor supply, wage rate, tax rate) and thus
+    is useful in HANK models (hence the name!).
+
+    Note 1: All time in this function runs forward, from t=0 to t=T
+
+    Note 2: All parameters are passed as attributes of the input parameters.
+
+    Parameters (passed as attributes of the input parameters)
+    ----------
+    PermShkStd : [float]
+        List of standard deviations in log permanent income uncertainty during
+        the agent's life.
+    PermShkCount : int
+        The number of approximation points to be used in the discrete approxima-
+        tion to the permanent income shock distribution.
+    TranShkStd : [float]
+        List of standard deviations in log transitory income uncertainty during
+        the agent's life.
+    TranShkCount : int
+        The number of approximation points to be used in the discrete approxima-
+        tion to the permanent income shock distribution.
+    UnempPrb : float or [float]
+        The probability of becoming unemployed during the working period.
+    UnempPrbRet : float or None
+        The probability of not receiving typical retirement income when retired.
+    T_retire : int
+        The index value for the final working period in the agent's life.
+        If T_retire <= 0 then there is no retirement.
+    IncUnemp : float or [float]
+        Transitory income received when unemployed.
+    IncUnempRet : float or None
+        Transitory income received while "unemployed" when retired.
+    tax_rate : [float]
+        List of flat tax rates on labor income, age-varying.
+    labor : [float]
+        List of intensive margin labor supply, age-varying.
+    wage : [float]
+        List of wage rate scaling factors, age-varying.
+    T_cycle :  int
+        Total number of non-terminal periods in the consumer's sequence of periods.
+
+    Returns
+    -------
+    IncShkDstn :  [distribution.Distribution]
+        A list with T_cycle elements, each of which is a
+        discrete approximation to the income process in a period.
+    PermShkDstn : [[distribution.Distributiony]]
+        A list with T_cycle elements, each of which
+        a discrete approximation to the permanent income shocks.
+    TranShkDstn : [[distribution.Distribution]]
+        A list with T_cycle elements, each of which
+        a discrete approximation to the transitory income shocks.
+    """
+    if T_retire > 0:
+        normal_length = T_retire
+        retire_length = T_cycle - T_retire
+    else:
+        normal_length = T_cycle
+        retire_length = 0
+
+    if all(
+        [
+            isinstance(x, (float, int)) or (x is None)
+            for x in [UnempPrb, IncUnemp, UnempPrbRet, IncUnempRet]
+        ]
+    ):
+        UnempPrb_list = [UnempPrb] * normal_length + [UnempPrbRet] * retire_length
+        IncUnemp_list = [IncUnemp] * normal_length + [IncUnempRet] * retire_length
+
+    elif all([isinstance(x, list) for x in [UnempPrb, IncUnemp]]):
+        UnempPrb_list = UnempPrb
+        IncUnemp_list = IncUnemp
+
+    else:
+        raise Exception(
+            "Unemployment must be specified either using floats for UnempPrb,"
+            + "IncUnemp, UnempPrbRet, and IncUnempRet, in which case the "
+            + "unemployment probability and income change only with retirement, or "
+            + "using lists of length T_cycle for UnempPrb and IncUnemp, specifying "
+            + "each feature at every age."
+        )
+
+    PermShkCount_list = [PermShkCount] * normal_length + [1] * retire_length
+    TranShkCount_list = [TranShkCount] * normal_length + [1] * retire_length
+    neutral_measure_list = [neutral_measure] * len(PermShkCount_list)
+
+    IncShkDstn = IndexDistribution(
+        engine=IncShkDstn_HANK,
+        conditional={
+            "sigma_Perm": PermShkStd,
+            "sigma_Tran": TranShkStd,
+            "n_approx_Perm": PermShkCount_list,
+            "n_approx_Tran": TranShkCount_list,
+            "neutral_measure": neutral_measure_list,
+            "UnempPrb": UnempPrb_list,
+            "IncUnemp": IncUnemp_list,
+            "wage": wage,
+            "tax_rate": tax_rate,
+            "labor": labor,
+        },
+        RNG=RNG,
+    )
+
+    return IncShkDstn
+
+
+###############################################################################
+
+
 def get_PermShkDstn_from_IncShkDstn(IncShkDstn, RNG):
     PermShkDstn = [
         this.make_univariate(0, seed=RNG.integers(0, 2**31 - 1)) for this in IncShkDstn
diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py
index 7d6a84a1d..b61da4929 100644
--- a/HARK/ConsumptionSaving/ConsIndShockModel.py
+++ b/HARK/ConsumptionSaving/ConsIndShockModel.py
@@ -57,15 +57,7 @@
     CRRAutilityPP,
     UtilityFuncCRRA,
 )
-from HARK.utilities import (
-    make_assets_grid,
-    gen_tran_matrix_1D,
-    gen_tran_matrix_2D,
-    jump_to_grid_1D,
-    jump_to_grid_2D,
-    make_grid_exp_mult,
-)
-from scipy import sparse as sp
+from HARK.utilities import make_assets_grid
 from scipy.optimize import newton
 
 from HARK import (
@@ -2058,684 +2050,6 @@ def get_shocks(self):
         self.shocks["PermShk"] = PermShkNow
         self.shocks["TranShk"] = TranShkNow
 
-    def define_distribution_grid(
-        self,
-        dist_mGrid=None,
-        dist_pGrid=None,
-        m_density=0,
-        num_pointsM=None,
-        timestonest=None,
-        num_pointsP=55,
-        max_p_fac=30.0,
-    ):
-        """
-        Defines the grid on which the distribution is defined. Stores the grid of market resources and permanent income as attributes of self.
-        Grid for normalized market resources and permanent income may be prespecified
-        as dist_mGrid and dist_pGrid, respectively. If not then default grid is computed based off given parameters.
-
-        Parameters
-        ----------
-        dist_mGrid : np.array
-                Prespecified grid for distribution over normalized market resources
-
-        dist_pGrid : np.array
-                Prespecified grid for distribution over permanent income.
-
-        m_density: float
-                Density of normalized market resources grid. Default value is mdensity = 0.
-                Only affects grid of market resources if dist_mGrid=None.
-
-        num_pointsM: float
-                Number of gridpoints for market resources grid.
-
-        num_pointsP: float
-                 Number of gridpoints for permanent income.
-                 This grid will be exponentiated by the function make_grid_exp_mult.
-
-        max_p_fac : float
-                Factor that scales the maximum value of permanent income grid.
-                Larger values increases the maximum value of permanent income grid.
-
-        Returns
-        -------
-        None
-        """
-
-        # If true Use Harmenberg 2021's Neutral Measure. For more information, see https://econ-ark.org/materials/harmenberg-aggregation?launch
-        if not hasattr(self, "neutral_measure"):
-            self.neutral_measure = False
-
-        if num_pointsM is None:
-            m_points = self.mCount
-        else:
-            m_points = num_pointsM
-
-        if not isinstance(timestonest, int):
-            timestonest = self.mFac
-        else:
-            timestonest = timestonest
-
-        if self.cycles == 0:
-            if not hasattr(dist_mGrid, "__len__"):
-                mGrid = make_grid_exp_mult(
-                    ming=self.mMin,
-                    maxg=self.mMax,
-                    ng=m_points,
-                    timestonest=timestonest,
-                )  # Generate Market resources grid given density and number of points
-
-                for i in range(m_density):
-                    m_shifted = np.delete(mGrid, -1)
-                    m_shifted = np.insert(m_shifted, 0, 1.00000000e-04)
-                    dist_betw_pts = mGrid - m_shifted
-                    dist_betw_pts_half = dist_betw_pts / 2
-                    new_A_grid = m_shifted + dist_betw_pts_half
-                    mGrid = np.concatenate((mGrid, new_A_grid))
-                    mGrid = np.sort(mGrid)
-
-                self.dist_mGrid = mGrid
-
-            else:
-                # If grid of market resources prespecified then use as mgrid
-                self.dist_mGrid = dist_mGrid
-
-            if not hasattr(dist_pGrid, "__len__"):
-                num_points = num_pointsP  # Number of permanent income gridpoints
-                # Dist_pGrid is taken to cover most of the ergodic distribution
-                # set variance of permanent income shocks
-                p_variance = self.PermShkStd[0] ** 2
-                # Maximum Permanent income value
-                max_p = max_p_fac * (p_variance / (1 - self.LivPrb[0])) ** 0.5
-                one_sided_grid = make_grid_exp_mult(
-                    1.05 + 1e-3, np.exp(max_p), num_points, 3
-                )
-                self.dist_pGrid = np.append(
-                    np.append(1.0 / np.fliplr([one_sided_grid])[0], np.ones(1)),
-                    one_sided_grid,
-                )  # Compute permanent income grid
-            else:
-                # If grid of permanent income prespecified then use it as pgrid
-                self.dist_pGrid = dist_pGrid
-
-            if (
-                self.neutral_measure is True
-            ):  # If true Use Harmenberg 2021's Neutral Measure. For more information, see https://econ-ark.org/materials/harmenberg-aggregation?launch
-                self.dist_pGrid = np.array([1])
-
-        elif self.cycles > 1:
-            raise Exception(
-                "define_distribution_grid requires cycles = 0 or cycles = 1"
-            )
-
-        elif self.T_cycle != 0:
-            if num_pointsM is None:
-                m_points = self.mCount
-            else:
-                m_points = num_pointsM
-
-            if not hasattr(dist_mGrid, "__len__"):
-                mGrid = make_grid_exp_mult(
-                    ming=self.mMin,
-                    maxg=self.mMax,
-                    ng=m_points,
-                    timestonest=timestonest,
-                )  # Generate Market resources grid given density and number of points
-
-                for i in range(m_density):
-                    m_shifted = np.delete(mGrid, -1)
-                    m_shifted = np.insert(m_shifted, 0, 1.00000000e-04)
-                    dist_betw_pts = mGrid - m_shifted
-                    dist_betw_pts_half = dist_betw_pts / 2
-                    new_A_grid = m_shifted + dist_betw_pts_half
-                    mGrid = np.concatenate((mGrid, new_A_grid))
-                    mGrid = np.sort(mGrid)
-
-                self.dist_mGrid = mGrid
-
-            else:
-                # If grid of market resources prespecified then use as mgrid
-                self.dist_mGrid = dist_mGrid
-
-            if not hasattr(dist_pGrid, "__len__"):
-                self.dist_pGrid = []  # list of grids of permanent income
-
-                for i in range(self.T_cycle):
-                    num_points = num_pointsP
-                    # Dist_pGrid is taken to cover most of the ergodic distribution
-                    # set variance of permanent income shocks this period
-                    p_variance = self.PermShkStd[i] ** 2
-                    # Consider probability of staying alive this period
-                    max_p = max_p_fac * (p_variance / (1 - self.LivPrb[i])) ** 0.5
-                    one_sided_grid = make_grid_exp_mult(
-                        1.05 + 1e-3, np.exp(max_p), num_points, 2
-                    )
-
-                    # Compute permanent income grid this period. Grid of permanent income may differ dependent on PermShkStd
-                    dist_pGrid = np.append(
-                        np.append(1.0 / np.fliplr([one_sided_grid])[0], np.ones(1)),
-                        one_sided_grid,
-                    )
-                    self.dist_pGrid.append(dist_pGrid)
-
-            else:
-                # If grid of permanent income prespecified then use as pgrid
-                self.dist_pGrid = dist_pGrid
-
-            if (
-                self.neutral_measure is True
-            ):  # If true Use Harmenberg 2021's Neutral Measure. For more information, see https://econ-ark.org/materials/harmenberg-aggregation?launch
-                self.dist_pGrid = self.T_cycle * [np.array([1])]
-
-    def calc_transition_matrix(self, shk_dstn=None):
-        """
-        Calculates how the distribution of agents across market resources
-        transitions from one period to the next. If finite horizon problem, then calculates
-        a list of transition matrices, consumption and asset policy grids for each period of the problem.
-        The transition matrix/matrices and consumption and asset policy grid(s) are stored as attributes of self.
-
-
-        Parameters
-        ----------
-            shk_dstn: list
-                list of income shock distributions. Each Income Shock Distribution should be a DiscreteDistribution Object (see Distribution.py)
-        Returns
-        -------
-        None
-
-        """
-
-        if self.cycles == 0:  # Infinite Horizon Problem
-            if not hasattr(shk_dstn, "pmv"):
-                shk_dstn = self.IncShkDstn
-
-            dist_mGrid = self.dist_mGrid  # Grid of market resources
-            dist_pGrid = self.dist_pGrid  # Grid of permanent incomes
-            # assets next period
-            aNext = dist_mGrid - self.solution[0].cFunc(dist_mGrid)
-
-            self.aPol_Grid = aNext  # Steady State Asset Policy Grid
-            # Steady State Consumption Policy Grid
-            self.cPol_Grid = self.solution[0].cFunc(dist_mGrid)
-
-            # Obtain shock values and shock probabilities from income distribution
-            # Bank Balances next period (Interest rate * assets)
-            bNext = self.Rfree * aNext
-            shk_prbs = shk_dstn[0].pmv  # Probability of shocks
-            tran_shks = shk_dstn[0].atoms[1]  # Transitory shocks
-            perm_shks = shk_dstn[0].atoms[0]  # Permanent shocks
-            LivPrb = self.LivPrb[0]  # Update probability of staying alive
-
-            # New borns have this distribution (assumes start with no assets and permanent income=1)
-            NewBornDist = jump_to_grid_2D(
-                tran_shks, np.ones_like(tran_shks), shk_prbs, dist_mGrid, dist_pGrid
-            )
-
-            if len(dist_pGrid) == 1:
-                NewBornDist = jump_to_grid_1D(
-                    np.ones_like(tran_shks), shk_prbs, dist_mGrid
-                )
-                # Compute Transition Matrix given shocks and grids.
-                self.tran_matrix = gen_tran_matrix_1D(
-                    dist_mGrid,
-                    bNext,
-                    shk_prbs,
-                    perm_shks,
-                    tran_shks,
-                    LivPrb,
-                    NewBornDist,
-                )
-
-            else:
-                NewBornDist = jump_to_grid_2D(
-                    np.ones_like(tran_shks),
-                    np.ones_like(tran_shks),
-                    shk_prbs,
-                    dist_mGrid,
-                    dist_pGrid,
-                )
-
-                # Generate Transition Matrix
-                # Compute Transition Matrix given shocks and grids.
-                self.tran_matrix = gen_tran_matrix_2D(
-                    dist_mGrid,
-                    dist_pGrid,
-                    bNext,
-                    shk_prbs,
-                    perm_shks,
-                    tran_shks,
-                    LivPrb,
-                    NewBornDist,
-                )
-
-        elif self.cycles > 1:
-            raise Exception("calc_transition_matrix requires cycles = 0 or cycles = 1")
-
-        elif self.T_cycle != 0:  # finite horizon problem
-            if not hasattr(shk_dstn, "pmv"):
-                shk_dstn = self.IncShkDstn
-
-            self.cPol_Grid = []
-            # List of consumption policy grids for each period in T_cycle
-            self.aPol_Grid = []
-            # List of asset policy grids for each period in T_cycle
-            self.tran_matrix = []  # List of transition matrices
-
-            dist_mGrid = self.dist_mGrid
-
-            for k in range(self.T_cycle):
-                if type(self.dist_pGrid) == list:
-                    # Permanent income grid this period
-                    dist_pGrid = self.dist_pGrid[k]
-                else:
-                    dist_pGrid = (
-                        self.dist_pGrid
-                    )  # If here then use prespecified permanent income grid
-
-                # Consumption policy grid in period k
-                Cnow = self.solution[k].cFunc(dist_mGrid)
-                self.cPol_Grid.append(Cnow)  # Add to list
-
-                aNext = dist_mGrid - Cnow  # Asset policy grid in period k
-                self.aPol_Grid.append(aNext)  # Add to list
-
-                if type(self.Rfree) == list:
-                    bNext = self.Rfree[k] * aNext
-                else:
-                    bNext = self.Rfree * aNext
-
-                # Obtain shocks and shock probabilities from income distribution this period
-                shk_prbs = shk_dstn[k].pmv  # Probability of shocks this period
-                # Transitory shocks this period
-                tran_shks = shk_dstn[k].atoms[1]
-                # Permanent shocks this period
-                perm_shks = shk_dstn[k].atoms[0]
-                # Update probability of staying alive this period
-                LivPrb = self.LivPrb[k]
-
-                if len(dist_pGrid) == 1:
-                    # New borns have this distribution (assumes start with no assets and permanent income=1)
-                    NewBornDist = jump_to_grid_1D(
-                        np.ones_like(tran_shks), shk_prbs, dist_mGrid
-                    )
-                    # Compute Transition Matrix given shocks and grids.
-                    TranMatrix_M = gen_tran_matrix_1D(
-                        dist_mGrid,
-                        bNext,
-                        shk_prbs,
-                        perm_shks,
-                        tran_shks,
-                        LivPrb,
-                        NewBornDist,
-                    )
-                    self.tran_matrix.append(TranMatrix_M)
-
-                else:
-                    NewBornDist = jump_to_grid_2D(
-                        np.ones_like(tran_shks),
-                        np.ones_like(tran_shks),
-                        shk_prbs,
-                        dist_mGrid,
-                        dist_pGrid,
-                    )
-                    # Compute Transition Matrix given shocks and grids.
-                    TranMatrix = gen_tran_matrix_2D(
-                        dist_mGrid,
-                        dist_pGrid,
-                        bNext,
-                        shk_prbs,
-                        perm_shks,
-                        tran_shks,
-                        LivPrb,
-                        NewBornDist,
-                    )
-                    self.tran_matrix.append(TranMatrix)
-
-    def calc_ergodic_dist(self, transition_matrix=None):
-        """
-        Calculates the ergodic distribution across normalized market resources and
-        permanent income as the eigenvector associated with the eigenvalue 1.
-        The distribution is stored as attributes of self both as a vector and as a reshaped array with the ij'th element representing
-        the probability of being at the i'th point on the mGrid and the j'th
-        point on the pGrid.
-
-        Parameters
-        ----------
-        transition_matrix: List
-                    list with one transition matrix whose ergordic distribution is to be solved
-        Returns
-        -------
-        None
-        """
-
-        if not isinstance(transition_matrix, list):
-            transition_matrix = [self.tran_matrix]
-
-        eigen, ergodic_distr = sp.linalg.eigs(
-            transition_matrix[0], v0=np.ones(len(transition_matrix[0])), k=1, which="LM"
-        )  # Solve for ergodic distribution
-        ergodic_distr = ergodic_distr.real / np.sum(ergodic_distr.real)
-
-        self.vec_erg_dstn = ergodic_distr  # distribution as a vector
-        # distribution reshaped into len(mgrid) by len(pgrid) array
-        self.erg_dstn = ergodic_distr.reshape(
-            (len(self.dist_mGrid), len(self.dist_pGrid))
-        )
-
-    def compute_steady_state(self):
-        # Compute steady state to perturb around
-        self.cycles = 0
-        self.solve()
-
-        # Use Harmenberg Measure
-        self.neutral_measure = True
-        self.update_income_process()
-
-        # Non stochastic simuation
-        self.define_distribution_grid()
-        self.calc_transition_matrix()
-
-        self.c_ss = self.cPol_Grid  # Normalized Consumption Policy grid
-        self.a_ss = self.aPol_Grid  # Normalized Asset Policy grid
-
-        self.calc_ergodic_dist()  # Calculate ergodic distribution
-        # Steady State Distribution as a vector (m*p x 1) where m is the number of gridpoints on the market resources grid
-        ss_dstn = self.vec_erg_dstn
-
-        self.A_ss = np.dot(self.a_ss, ss_dstn)[0]
-        self.C_ss = np.dot(self.c_ss, ss_dstn)[0]
-
-        return self.A_ss, self.C_ss
-
-    def calc_jacobian(self, shk_param, T):
-        """
-        Calculates the Jacobians of aggregate consumption and aggregate assets.
-        Parameters that can be shocked are LivPrb, PermShkStd,TranShkStd, DiscFac,
-        UnempPrb, Rfree, IncUnemp, and DiscFac.
-
-        Parameters:
-        -----------
-
-        shk_param: string
-            name of variable to be shocked
-
-        T: int
-            dimension of Jacobian Matrix. Jacobian Matrix is a TxT square Matrix
-
-
-        Returns
-        ----------
-        CJAC: numpy.array
-            TxT Jacobian Matrix of Aggregate Consumption with respect to shk_param
-
-        AJAC: numpy.array
-            TxT Jacobian Matrix of Aggregate Assets with respect to shk_param
-
-        """
-
-        # Set up finite Horizon dictionary
-        params = deepcopy(self.__dict__["parameters"])
-        params["T_cycle"] = T  # Dimension of Jacobian Matrix
-
-        # Specify a dictionary of lists because problem we are solving is
-        # technically finite horizon so variables can be time varying (see
-        # section on fake news algorithm in
-        # https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA17434 )
-        params["LivPrb"] = params["T_cycle"] * [self.LivPrb[0]]
-        params["PermGroFac"] = params["T_cycle"] * [self.PermGroFac[0]]
-        params["PermShkStd"] = params["T_cycle"] * [self.PermShkStd[0]]
-        params["TranShkStd"] = params["T_cycle"] * [self.TranShkStd[0]]
-        params["Rfree"] = params["T_cycle"] * [self.Rfree]
-        params["UnempPrb"] = params["T_cycle"] * [self.UnempPrb]
-        params["IncUnemp"] = params["T_cycle"] * [self.IncUnemp]
-
-        # Create instance of a finite horizon agent
-        FinHorizonAgent = IndShockConsumerType(**params)
-        FinHorizonAgent.cycles = 1  # required
-
-        # delete Rfree from time invariant list since it varies overtime
-        FinHorizonAgent.del_from_time_inv("Rfree")
-        # Add Rfree to time varying list to be able to introduce time varying interest rates
-        FinHorizonAgent.add_to_time_vary("Rfree")
-
-        # Set Terminal Solution as Steady State Consumption Function
-        FinHorizonAgent.solution_terminal = deepcopy(self.solution[0])
-
-        dx = 0.0001  # Size of perturbation
-        # Period in which the change in the interest rate occurs (second to last period)
-        i = params["T_cycle"] - 1
-
-        FinHorizonAgent.IncShkDstn = params["T_cycle"] * [self.IncShkDstn[0]]
-
-        # If parameter is in time invariant list then add it to time vary list
-        FinHorizonAgent.del_from_time_inv(shk_param)
-        FinHorizonAgent.add_to_time_vary(shk_param)
-
-        # this condition is because some attributes are specified as lists while other as floats
-        if type(getattr(self, shk_param)) == list:
-            perturbed_list = (
-                (i) * [getattr(self, shk_param)[0]]
-                + [getattr(self, shk_param)[0] + dx]
-                + (params["T_cycle"] - i - 1) * [getattr(self, shk_param)[0]]
-            )  # Sequence of interest rates the agent faces
-        else:
-            perturbed_list = (
-                (i) * [getattr(self, shk_param)]
-                + [getattr(self, shk_param) + dx]
-                + (params["T_cycle"] - i - 1) * [getattr(self, shk_param)]
-            )  # Sequence of interest rates the agent faces
-        setattr(FinHorizonAgent, shk_param, perturbed_list)
-        self.parameters[shk_param] = perturbed_list
-
-        # Update income process if perturbed parameter enters the income shock distribution
-        FinHorizonAgent.update_income_process()
-
-        # Solve
-        FinHorizonAgent.solve(run_presolve=False)
-
-        # Use Harmenberg Neutral Measure
-        FinHorizonAgent.neutral_measure = True
-        FinHorizonAgent.update_income_process()
-
-        # Calculate Transition Matrices
-        FinHorizonAgent.define_distribution_grid()
-        FinHorizonAgent.calc_transition_matrix()
-
-        # Normalized consumption Policy Grids across time
-        c_t = FinHorizonAgent.cPol_Grid
-        a_t = FinHorizonAgent.aPol_Grid
-
-        # Append steady state policy grid into list of policy grids as HARK does not provide the initial policy
-        c_t.append(self.c_ss)
-        a_t.append(self.a_ss)
-
-        # Fake News Algorithm begins below ( To find fake news algorithm See page 2388 of https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA17434  )
-
-        ##########
-        # STEP 1 # of fake news algorithm, As in the paper for Curly Y and Curly D. Here the policies are over assets and consumption so we denote them as curly C and curly D.
-        ##########
-        a_ss = self.aPol_Grid  # steady state Asset Policy
-        c_ss = self.cPol_Grid  # steady state Consumption Policy
-        tranmat_ss = self.tran_matrix  # Steady State Transition Matrix
-
-        # List of asset policies grids where households expect the shock to occur in the second to last Period
-        a_t = FinHorizonAgent.aPol_Grid
-        # add steady state assets to list as it does not get appended in calc_transition_matrix method
-        a_t.append(self.a_ss)
-
-        # List of consumption policies grids where households expect the shock to occur in the second to last Period
-        c_t = FinHorizonAgent.cPol_Grid
-        # add steady state consumption to list as it does not get appended in calc_transition_matrix method
-        c_t.append(self.c_ss)
-
-        da0_s = []  # Deviation of asset policy from steady state policy
-        dc0_s = []  # Deviation of Consumption policy from steady state policy
-        for i in range(T):
-            da0_s.append(a_t[T - i] - a_ss)
-            dc0_s.append(c_t[T - i] - c_ss)
-
-        da0_s = np.array(da0_s)
-        dc0_s = np.array(dc0_s)
-
-        # Steady state distribution of market resources (permanent income weighted distribution)
-        D_ss = self.vec_erg_dstn.T[0]
-        dA0_s = []
-        dC0_s = []
-        for i in range(T):
-            dA0_s.append(np.dot(da0_s[i], D_ss))
-            dC0_s.append(np.dot(dc0_s[i], D_ss))
-
-        dA0_s = np.array(dA0_s)
-        # This is equivalent to the curly Y scalar detailed in the first step of the algorithm
-        A_curl_s = dA0_s / dx
-
-        dC0_s = np.array(dC0_s)
-        C_curl_s = dC0_s / dx
-
-        # List of computed transition matrices for each period
-        tranmat_t = FinHorizonAgent.tran_matrix
-        tranmat_t.append(tranmat_ss)
-
-        # List of change in transition matrix relative to the steady state transition matrix
-        dlambda0_s = []
-        for i in range(T):
-            dlambda0_s.append(tranmat_t[T - i] - tranmat_ss)
-
-        dlambda0_s = np.array(dlambda0_s)
-
-        dD0_s = []
-        for i in range(T):
-            dD0_s.append(np.dot(dlambda0_s[i], D_ss))
-
-        dD0_s = np.array(dD0_s)
-        D_curl_s = dD0_s / dx  # Curly D in the sequence space jacobian
-
-        ########
-        # STEP2 # of fake news algorithm
-        ########
-
-        # Expectation Vectors
-        exp_vecs_a = []
-        exp_vecs_c = []
-
-        # First expectation vector is the steady state policy
-        exp_vec_a = a_ss
-        exp_vec_c = c_ss
-        for i in range(T):
-            exp_vecs_a.append(exp_vec_a)
-            exp_vec_a = np.dot(tranmat_ss.T, exp_vec_a)
-
-            exp_vecs_c.append(exp_vec_c)
-            exp_vec_c = np.dot(tranmat_ss.T, exp_vec_c)
-
-        # Turn expectation vectors into arrays
-        exp_vecs_a = np.array(exp_vecs_a)
-        exp_vecs_c = np.array(exp_vecs_c)
-
-        #########
-        # STEP3 # of the algorithm. In particular equation 26 of the published paper.
-        #########
-        # Fake news matrices
-        Curl_F_A = np.zeros((T, T))  # Fake news matrix for assets
-        Curl_F_C = np.zeros((T, T))  # Fake news matrix for consumption
-
-        # First row of Fake News Matrix
-        Curl_F_A[0] = A_curl_s
-        Curl_F_C[0] = C_curl_s
-
-        for i in range(T - 1):
-            for j in range(T):
-                Curl_F_A[i + 1][j] = np.dot(exp_vecs_a[i], D_curl_s[j])
-                Curl_F_C[i + 1][j] = np.dot(exp_vecs_c[i], D_curl_s[j])
-
-        ########
-        # STEP4 #  of the algorithm
-        ########
-
-        # Function to compute jacobian matrix from fake news matrix
-        def J_from_F(F):
-            J = F.copy()
-            for t in range(1, F.shape[0]):
-                J[1:, t] += J[:-1, t - 1]
-            return J
-
-        J_A = J_from_F(Curl_F_A)
-        J_C = J_from_F(Curl_F_C)
-
-        ########
-        # Additional step due to compute Zeroth Column of the Jacobian
-        ########
-
-        params = deepcopy(self.__dict__["parameters"])
-        params["T_cycle"] = 2  # Dimension of Jacobian Matrix
-
-        params["LivPrb"] = params["T_cycle"] * [self.LivPrb[0]]
-        params["PermGroFac"] = params["T_cycle"] * [self.PermGroFac[0]]
-        params["PermShkStd"] = params["T_cycle"] * [self.PermShkStd[0]]
-        params["TranShkStd"] = params["T_cycle"] * [self.TranShkStd[0]]
-        params["Rfree"] = params["T_cycle"] * [self.Rfree]
-        params["UnempPrb"] = params["T_cycle"] * [self.UnempPrb]
-        params["IncUnemp"] = params["T_cycle"] * [self.IncUnemp]
-        params["IncShkDstn"] = params["T_cycle"] * [self.IncShkDstn[0]]
-        params["cFunc_terminal_"] = deepcopy(self.solution[0].cFunc)
-
-        # Create instance of a finite horizon agent for calculation of zeroth
-        ZerothColAgent = IndShockConsumerType(**params)
-        ZerothColAgent.cycles = 1  # required
-
-        # If parameter is in time invariant list then add it to time vary list
-        ZerothColAgent.del_from_time_inv(shk_param)
-        ZerothColAgent.add_to_time_vary(shk_param)
-
-        # Update income process if perturbed parameter enters the income shock distribution
-        ZerothColAgent.update_income_process()
-
-        # Solve
-        ZerothColAgent.solve()
-
-        # this condition is because some attributes are specified as lists while other as floats
-        if type(getattr(self, shk_param)) == list:
-            perturbed_list = [getattr(self, shk_param)[0] + dx] + (
-                params["T_cycle"] - 1
-            ) * [
-                getattr(self, shk_param)[0]
-            ]  # Sequence of interest rates the agent faces
-        else:
-            perturbed_list = [getattr(self, shk_param) + dx] + (
-                params["T_cycle"] - 1
-            ) * [getattr(self, shk_param)]
-            # Sequence of interest rates the agent
-
-        setattr(ZerothColAgent, shk_param, perturbed_list)  # Set attribute to agent
-        self.parameters[shk_param] = perturbed_list
-
-        # Use Harmenberg Neutral Measure
-        ZerothColAgent.neutral_measure = True
-        ZerothColAgent.update_income_process()
-
-        # Calculate Transition Matrices
-        ZerothColAgent.define_distribution_grid()
-        ZerothColAgent.calc_transition_matrix()
-
-        tranmat_t_zeroth_col = ZerothColAgent.tran_matrix
-        dstn_t_zeroth_col = self.vec_erg_dstn.T[0]
-
-        C_t_no_sim = np.zeros(T)
-        A_t_no_sim = np.zeros(T)
-
-        for i in range(T):
-            if i == 0:
-                dstn_t_zeroth_col = np.dot(tranmat_t_zeroth_col[i], dstn_t_zeroth_col)
-            else:
-                dstn_t_zeroth_col = np.dot(tranmat_ss, dstn_t_zeroth_col)
-
-            C_t_no_sim[i] = np.dot(self.cPol_Grid, dstn_t_zeroth_col)
-            A_t_no_sim[i] = np.dot(self.aPol_Grid, dstn_t_zeroth_col)
-
-        J_A.T[0] = (A_t_no_sim - self.A_ss) / dx
-        J_C.T[0] = (C_t_no_sim - self.C_ss) / dx
-
-        return J_C, J_A
-
     def make_euler_error_func(self, mMax=100, approx_inc_dstn=True):
         """
         Creates a "normalized Euler error" function for this instance, mapping
diff --git a/HARK/ConsumptionSaving/ConsNewKeynesianModel.py b/HARK/ConsumptionSaving/ConsNewKeynesianModel.py
new file mode 100644
index 000000000..605b26f26
--- /dev/null
+++ b/HARK/ConsumptionSaving/ConsNewKeynesianModel.py
@@ -0,0 +1,798 @@
+"""
+This file has a slightly modified and extended version of ConsIndShock that is
+meant to be used in heterogeneous agents new Keynesian (HANK) models. The micro-
+economic model is identical, but additional primitive parameters have been added
+to the specification of the income process. These parameters would have no inde-
+pendent meaning in a "micro only" setting, but with dynamic equilibrium elements
+(as in HANK models), they can have meaning.
+"""
+
+from copy import deepcopy
+import numpy as np
+from scipy import sparse as sp
+
+from HARK.ConsumptionSaving.ConsIndShockModel import (
+    IndShockConsumerType,
+    make_basic_CRRA_solution_terminal,
+)
+
+from HARK.Calibration.Income.IncomeProcesses import (
+    construct_HANK_lognormal_income_process_unemployment,
+    get_PermShkDstn_from_IncShkDstn,
+    get_TranShkDstn_from_IncShkDstn,
+)
+
+from HARK.utilities import (
+    gen_tran_matrix_1D,
+    gen_tran_matrix_2D,
+    jump_to_grid_1D,
+    jump_to_grid_2D,
+    make_grid_exp_mult,
+    make_assets_grid,
+)
+
+# Make a dictionary of constructors for the idiosyncratic income shocks model
+newkeynesian_constructor_dict = {
+    "IncShkDstn": construct_HANK_lognormal_income_process_unemployment,
+    "PermShkDstn": get_PermShkDstn_from_IncShkDstn,
+    "TranShkDstn": get_TranShkDstn_from_IncShkDstn,
+    "aXtraGrid": make_assets_grid,
+    "solution_terminal": make_basic_CRRA_solution_terminal,
+}
+
+# Default parameters to make IncShkDstn using construct_lognormal_income_process_unemployment
+default_IncShkDstn_params = {
+    "PermShkStd": [0.1],  # Standard deviation of log permanent income shocks
+    "PermShkCount": 7,  # Number of points in discrete approximation to permanent income shocks
+    "TranShkStd": [0.1],  # Standard deviation of log transitory income shocks
+    "TranShkCount": 7,  # Number of points in discrete approximation to transitory income shocks
+    "UnempPrb": 0.05,  # Probability of unemployment while working
+    "IncUnemp": 0.3,  # Unemployment benefits replacement rate while working
+    "T_retire": 0,  # Period of retirement (0 --> no retirement)
+    "UnempPrbRet": 0.005,  # Probability of "unemployment" while retired
+    "IncUnempRet": 0.0,  # "Unemployment" benefits when retired
+    "tax_rate": [0.0],  # Flat tax rate on labor income NEW FOR HANK
+    "labor": [1.0],  # Intensive margin labor supply NEW FOR HANK
+    "wage": [1.0],  # Wage rate scaling factor NEW FOR HANK
+}
+
+# Default parameters to make aXtraGrid using make_assets_grid
+default_aXtraGrid_params = {
+    "aXtraMin": 0.001,  # Minimum end-of-period "assets above minimum" value
+    "aXtraMax": 20,  # Maximum end-of-period "assets above minimum" value
+    "aXtraNestFac": 3,  # Exponential nesting factor for aXtraGrid
+    "aXtraCount": 48,  # Number of points in the grid of "assets above minimum"
+    "aXtraExtra": None,  # Additional other values to add in grid (optional)
+}
+
+# Make a dictionary to specify an idiosyncratic income shocks consumer type
+init_newkeynesian = {
+    # BASIC HARK PARAMETERS REQUIRED TO SOLVE THE MODEL
+    "cycles": 1,  # Finite, non-cyclic model
+    "T_cycle": 1,  # Number of periods in the cycle for this agent type
+    "constructors": newkeynesian_constructor_dict,  # See dictionary above
+    # PRIMITIVE RAW PARAMETERS REQUIRED TO SOLVE THE MODEL
+    "CRRA": 2.0,  # Coefficient of relative risk aversion
+    "Rfree": 1.03,  # Interest factor on retained assets
+    "DiscFac": 0.96,  # Intertemporal discount factor
+    "LivPrb": [0.98],  # Survival probability after each period
+    "PermGroFac": [1.01],  # Permanent income growth factor
+    "BoroCnstArt": 0.0,  # Artificial borrowing constraint
+    "vFuncBool": False,  # Whether to calculate the value function during solution
+    "CubicBool": False,  # Whether to use cubic spline interpolation when True
+    # (Uses linear spline interpolation for cFunc when False)
+    # PARAMETERS REQUIRED TO SIMULATE THE MODEL
+    "AgentCount": 10000,  # Number of agents of this type
+    "T_age": None,  # Age after which simulated agents are automatically killed
+    "aNrmInitMean": 0.0,  # Mean of log initial assets
+    "aNrmInitStd": 1.0,  # Standard deviation of log initial assets
+    "pLvlInitMean": 0.0,  # Mean of log initial permanent income
+    "pLvlInitStd": 0.0,  # Standard deviation of log initial permanent income
+    "PermGroFacAgg": 1.0,  # Aggregate permanent income growth factor
+    # (The portion of PermGroFac attributable to aggregate productivity growth)
+    "NewbornTransShk": False,  # Whether Newborns have transitory shock
+    # ADDITIONAL OPTIONAL PARAMETERS
+    "PerfMITShk": False,  # Do Perfect Foresight MIT Shock
+    # (Forces Newborns to follow solution path of the agent they replaced if True)
+    "neutral_measure": False,  # Whether to use permanent income neutral measure (see Harmenberg 2021)
+}
+init_newkeynesian.update(default_IncShkDstn_params)
+init_newkeynesian.update(default_aXtraGrid_params)
+
+
+class NewKeynesianConsumerType(IndShockConsumerType):
+    """
+    A slight extension of IndShockConsumerType that permits individual labor supply,
+    the wage rate, and the labor income tax rate to enter the income shock process.
+    """
+
+    def __init__(self, verbose=1, quiet=False, **kwds):
+        params = init_newkeynesian.copy()
+        params.update(kwds)
+
+        # Run the basic initializer
+        super().__init__(verbose=verbose, quiet=quiet, **params)
+
+    def define_distribution_grid(
+        self,
+        dist_mGrid=None,
+        dist_pGrid=None,
+        m_density=0,
+        num_pointsM=None,
+        timestonest=None,
+        num_pointsP=55,
+        max_p_fac=30.0,
+    ):
+        """
+        Defines the grid on which the distribution is defined. Stores the grid of market resources and permanent income as attributes of self.
+        Grid for normalized market resources and permanent income may be prespecified
+        as dist_mGrid and dist_pGrid, respectively. If not then default grid is computed based off given parameters.
+
+        Parameters
+        ----------
+        dist_mGrid : np.array
+                Prespecified grid for distribution over normalized market resources
+
+        dist_pGrid : np.array
+                Prespecified grid for distribution over permanent income.
+
+        m_density: float
+                Density of normalized market resources grid. Default value is mdensity = 0.
+                Only affects grid of market resources if dist_mGrid=None.
+
+        num_pointsM: float
+                Number of gridpoints for market resources grid.
+
+        num_pointsP: float
+                 Number of gridpoints for permanent income.
+                 This grid will be exponentiated by the function make_grid_exp_mult.
+
+        max_p_fac : float
+                Factor that scales the maximum value of permanent income grid.
+                Larger values increases the maximum value of permanent income grid.
+
+        Returns
+        -------
+        None
+        """
+
+        # If true Use Harmenberg 2021's Neutral Measure. For more information, see https://econ-ark.org/materials/harmenberg-aggregation?launch
+        if not hasattr(self, "neutral_measure"):
+            self.neutral_measure = False
+
+        if num_pointsM is None:
+            m_points = self.mCount
+        else:
+            m_points = num_pointsM
+
+        if not isinstance(timestonest, int):
+            timestonest = self.mFac
+        else:
+            timestonest = timestonest
+
+        if self.cycles == 0:
+            if not hasattr(dist_mGrid, "__len__"):
+                mGrid = make_grid_exp_mult(
+                    ming=self.mMin,
+                    maxg=self.mMax,
+                    ng=m_points,
+                    timestonest=timestonest,
+                )  # Generate Market resources grid given density and number of points
+
+                for i in range(m_density):
+                    m_shifted = np.delete(mGrid, -1)
+                    m_shifted = np.insert(m_shifted, 0, 1.00000000e-04)
+                    dist_betw_pts = mGrid - m_shifted
+                    dist_betw_pts_half = dist_betw_pts / 2
+                    new_A_grid = m_shifted + dist_betw_pts_half
+                    mGrid = np.concatenate((mGrid, new_A_grid))
+                    mGrid = np.sort(mGrid)
+
+                self.dist_mGrid = mGrid
+
+            else:
+                # If grid of market resources prespecified then use as mgrid
+                self.dist_mGrid = dist_mGrid
+
+            if not hasattr(dist_pGrid, "__len__"):
+                num_points = num_pointsP  # Number of permanent income gridpoints
+                # Dist_pGrid is taken to cover most of the ergodic distribution
+                # set variance of permanent income shocks
+                p_variance = self.PermShkStd[0] ** 2
+                # Maximum Permanent income value
+                max_p = max_p_fac * (p_variance / (1 - self.LivPrb[0])) ** 0.5
+                one_sided_grid = make_grid_exp_mult(
+                    1.05 + 1e-3, np.exp(max_p), num_points, 3
+                )
+                self.dist_pGrid = np.append(
+                    np.append(1.0 / np.fliplr([one_sided_grid])[0], np.ones(1)),
+                    one_sided_grid,
+                )  # Compute permanent income grid
+            else:
+                # If grid of permanent income prespecified then use it as pgrid
+                self.dist_pGrid = dist_pGrid
+
+            if (
+                self.neutral_measure is True
+            ):  # If true Use Harmenberg 2021's Neutral Measure. For more information, see https://econ-ark.org/materials/harmenberg-aggregation?launch
+                self.dist_pGrid = np.array([1])
+
+        elif self.cycles > 1:
+            raise Exception(
+                "define_distribution_grid requires cycles = 0 or cycles = 1"
+            )
+
+        elif self.T_cycle != 0:
+            if num_pointsM is None:
+                m_points = self.mCount
+            else:
+                m_points = num_pointsM
+
+            if not hasattr(dist_mGrid, "__len__"):
+                mGrid = make_grid_exp_mult(
+                    ming=self.mMin,
+                    maxg=self.mMax,
+                    ng=m_points,
+                    timestonest=timestonest,
+                )  # Generate Market resources grid given density and number of points
+
+                for i in range(m_density):
+                    m_shifted = np.delete(mGrid, -1)
+                    m_shifted = np.insert(m_shifted, 0, 1.00000000e-04)
+                    dist_betw_pts = mGrid - m_shifted
+                    dist_betw_pts_half = dist_betw_pts / 2
+                    new_A_grid = m_shifted + dist_betw_pts_half
+                    mGrid = np.concatenate((mGrid, new_A_grid))
+                    mGrid = np.sort(mGrid)
+
+                self.dist_mGrid = mGrid
+
+            else:
+                # If grid of market resources prespecified then use as mgrid
+                self.dist_mGrid = dist_mGrid
+
+            if not hasattr(dist_pGrid, "__len__"):
+                self.dist_pGrid = []  # list of grids of permanent income
+
+                for i in range(self.T_cycle):
+                    num_points = num_pointsP
+                    # Dist_pGrid is taken to cover most of the ergodic distribution
+                    # set variance of permanent income shocks this period
+                    p_variance = self.PermShkStd[i] ** 2
+                    # Consider probability of staying alive this period
+                    max_p = max_p_fac * (p_variance / (1 - self.LivPrb[i])) ** 0.5
+                    one_sided_grid = make_grid_exp_mult(
+                        1.05 + 1e-3, np.exp(max_p), num_points, 2
+                    )
+
+                    # Compute permanent income grid this period. Grid of permanent income may differ dependent on PermShkStd
+                    dist_pGrid = np.append(
+                        np.append(1.0 / np.fliplr([one_sided_grid])[0], np.ones(1)),
+                        one_sided_grid,
+                    )
+                    self.dist_pGrid.append(dist_pGrid)
+
+            else:
+                # If grid of permanent income prespecified then use as pgrid
+                self.dist_pGrid = dist_pGrid
+
+            if (
+                self.neutral_measure is True
+            ):  # If true Use Harmenberg 2021's Neutral Measure. For more information, see https://econ-ark.org/materials/harmenberg-aggregation?launch
+                self.dist_pGrid = self.T_cycle * [np.array([1])]
+
+    def calc_transition_matrix(self, shk_dstn=None):
+        """
+        Calculates how the distribution of agents across market resources
+        transitions from one period to the next. If finite horizon problem, then calculates
+        a list of transition matrices, consumption and asset policy grids for each period of the problem.
+        The transition matrix/matrices and consumption and asset policy grid(s) are stored as attributes of self.
+
+
+        Parameters
+        ----------
+            shk_dstn: list
+                list of income shock distributions. Each Income Shock Distribution should be a DiscreteDistribution Object (see Distribution.py)
+        Returns
+        -------
+        None
+
+        """
+
+        if self.cycles == 0:  # Infinite Horizon Problem
+            if not hasattr(shk_dstn, "pmv"):
+                shk_dstn = self.IncShkDstn
+
+            dist_mGrid = self.dist_mGrid  # Grid of market resources
+            dist_pGrid = self.dist_pGrid  # Grid of permanent incomes
+            # assets next period
+            aNext = dist_mGrid - self.solution[0].cFunc(dist_mGrid)
+
+            self.aPol_Grid = aNext  # Steady State Asset Policy Grid
+            # Steady State Consumption Policy Grid
+            self.cPol_Grid = self.solution[0].cFunc(dist_mGrid)
+
+            # Obtain shock values and shock probabilities from income distribution
+            # Bank Balances next period (Interest rate * assets)
+            bNext = self.Rfree * aNext
+            shk_prbs = shk_dstn[0].pmv  # Probability of shocks
+            tran_shks = shk_dstn[0].atoms[1]  # Transitory shocks
+            perm_shks = shk_dstn[0].atoms[0]  # Permanent shocks
+            LivPrb = self.LivPrb[0]  # Update probability of staying alive
+
+            # New borns have this distribution (assumes start with no assets and permanent income=1)
+            NewBornDist = jump_to_grid_2D(
+                tran_shks, np.ones_like(tran_shks), shk_prbs, dist_mGrid, dist_pGrid
+            )
+
+            if len(dist_pGrid) == 1:
+                NewBornDist = jump_to_grid_1D(
+                    np.ones_like(tran_shks), shk_prbs, dist_mGrid
+                )
+                # Compute Transition Matrix given shocks and grids.
+                self.tran_matrix = gen_tran_matrix_1D(
+                    dist_mGrid,
+                    bNext,
+                    shk_prbs,
+                    perm_shks,
+                    tran_shks,
+                    LivPrb,
+                    NewBornDist,
+                )
+
+            else:
+                NewBornDist = jump_to_grid_2D(
+                    np.ones_like(tran_shks),
+                    np.ones_like(tran_shks),
+                    shk_prbs,
+                    dist_mGrid,
+                    dist_pGrid,
+                )
+
+                # Generate Transition Matrix
+                # Compute Transition Matrix given shocks and grids.
+                self.tran_matrix = gen_tran_matrix_2D(
+                    dist_mGrid,
+                    dist_pGrid,
+                    bNext,
+                    shk_prbs,
+                    perm_shks,
+                    tran_shks,
+                    LivPrb,
+                    NewBornDist,
+                )
+
+        elif self.cycles > 1:
+            raise Exception("calc_transition_matrix requires cycles = 0 or cycles = 1")
+
+        elif self.T_cycle != 0:  # finite horizon problem
+            if not hasattr(shk_dstn, "pmv"):
+                shk_dstn = self.IncShkDstn
+
+            self.cPol_Grid = []
+            # List of consumption policy grids for each period in T_cycle
+            self.aPol_Grid = []
+            # List of asset policy grids for each period in T_cycle
+            self.tran_matrix = []  # List of transition matrices
+
+            dist_mGrid = self.dist_mGrid
+
+            for k in range(self.T_cycle):
+                if type(self.dist_pGrid) == list:
+                    # Permanent income grid this period
+                    dist_pGrid = self.dist_pGrid[k]
+                else:
+                    dist_pGrid = (
+                        self.dist_pGrid
+                    )  # If here then use prespecified permanent income grid
+
+                # Consumption policy grid in period k
+                Cnow = self.solution[k].cFunc(dist_mGrid)
+                self.cPol_Grid.append(Cnow)  # Add to list
+
+                aNext = dist_mGrid - Cnow  # Asset policy grid in period k
+                self.aPol_Grid.append(aNext)  # Add to list
+
+                if type(self.Rfree) == list:
+                    bNext = self.Rfree[k] * aNext
+                else:
+                    bNext = self.Rfree * aNext
+
+                # Obtain shocks and shock probabilities from income distribution this period
+                shk_prbs = shk_dstn[k].pmv  # Probability of shocks this period
+                # Transitory shocks this period
+                tran_shks = shk_dstn[k].atoms[1]
+                # Permanent shocks this period
+                perm_shks = shk_dstn[k].atoms[0]
+                # Update probability of staying alive this period
+                LivPrb = self.LivPrb[k]
+
+                if len(dist_pGrid) == 1:
+                    # New borns have this distribution (assumes start with no assets and permanent income=1)
+                    NewBornDist = jump_to_grid_1D(
+                        np.ones_like(tran_shks), shk_prbs, dist_mGrid
+                    )
+                    # Compute Transition Matrix given shocks and grids.
+                    TranMatrix_M = gen_tran_matrix_1D(
+                        dist_mGrid,
+                        bNext,
+                        shk_prbs,
+                        perm_shks,
+                        tran_shks,
+                        LivPrb,
+                        NewBornDist,
+                    )
+                    self.tran_matrix.append(TranMatrix_M)
+
+                else:
+                    NewBornDist = jump_to_grid_2D(
+                        np.ones_like(tran_shks),
+                        np.ones_like(tran_shks),
+                        shk_prbs,
+                        dist_mGrid,
+                        dist_pGrid,
+                    )
+                    # Compute Transition Matrix given shocks and grids.
+                    TranMatrix = gen_tran_matrix_2D(
+                        dist_mGrid,
+                        dist_pGrid,
+                        bNext,
+                        shk_prbs,
+                        perm_shks,
+                        tran_shks,
+                        LivPrb,
+                        NewBornDist,
+                    )
+                    self.tran_matrix.append(TranMatrix)
+
+    def calc_ergodic_dist(self, transition_matrix=None):
+        """
+        Calculates the ergodic distribution across normalized market resources and
+        permanent income as the eigenvector associated with the eigenvalue 1.
+        The distribution is stored as attributes of self both as a vector and as a reshaped array with the ij'th element representing
+        the probability of being at the i'th point on the mGrid and the j'th
+        point on the pGrid.
+
+        Parameters
+        ----------
+        transition_matrix: List
+                    list with one transition matrix whose ergordic distribution is to be solved
+        Returns
+        -------
+        None
+        """
+
+        if not isinstance(transition_matrix, list):
+            transition_matrix = [self.tran_matrix]
+
+        eigen, ergodic_distr = sp.linalg.eigs(
+            transition_matrix[0], v0=np.ones(len(transition_matrix[0])), k=1, which="LM"
+        )  # Solve for ergodic distribution
+        ergodic_distr = ergodic_distr.real / np.sum(ergodic_distr.real)
+
+        self.vec_erg_dstn = ergodic_distr  # distribution as a vector
+        # distribution reshaped into len(mgrid) by len(pgrid) array
+        self.erg_dstn = ergodic_distr.reshape(
+            (len(self.dist_mGrid), len(self.dist_pGrid))
+        )
+
+    def compute_steady_state(self):
+        # Compute steady state to perturb around
+        self.cycles = 0
+        self.solve()
+
+        # Use Harmenberg Measure
+        self.neutral_measure = True
+        self.update_income_process()
+
+        # Non stochastic simuation
+        self.define_distribution_grid()
+        self.calc_transition_matrix()
+
+        self.c_ss = self.cPol_Grid  # Normalized Consumption Policy grid
+        self.a_ss = self.aPol_Grid  # Normalized Asset Policy grid
+
+        self.calc_ergodic_dist()  # Calculate ergodic distribution
+        # Steady State Distribution as a vector (m*p x 1) where m is the number of gridpoints on the market resources grid
+        ss_dstn = self.vec_erg_dstn
+
+        self.A_ss = np.dot(self.a_ss, ss_dstn)[0]
+        self.C_ss = np.dot(self.c_ss, ss_dstn)[0]
+
+        return self.A_ss, self.C_ss
+
+    def calc_jacobian(self, shk_param, T):
+        """
+        Calculates the Jacobians of aggregate consumption and aggregate assets.
+        Parameters that can be shocked are LivPrb, PermShkStd,TranShkStd, DiscFac,
+        UnempPrb, Rfree, IncUnemp, and DiscFac.
+
+        Parameters:
+        -----------
+
+        shk_param: string
+            name of variable to be shocked
+
+        T: int
+            dimension of Jacobian Matrix. Jacobian Matrix is a TxT square Matrix
+
+
+        Returns
+        ----------
+        CJAC: numpy.array
+            TxT Jacobian Matrix of Aggregate Consumption with respect to shk_param
+
+        AJAC: numpy.array
+            TxT Jacobian Matrix of Aggregate Assets with respect to shk_param
+
+        """
+
+        # Set up finite Horizon dictionary
+        params = deepcopy(self.__dict__["parameters"])
+        params["T_cycle"] = T  # Dimension of Jacobian Matrix
+
+        # Specify a dictionary of lists because problem we are solving is
+        # technically finite horizon so variables can be time varying (see
+        # section on fake news algorithm in
+        # https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA17434 )
+        params["LivPrb"] = params["T_cycle"] * [self.LivPrb[0]]
+        params["PermGroFac"] = params["T_cycle"] * [self.PermGroFac[0]]
+        params["PermShkStd"] = params["T_cycle"] * [self.PermShkStd[0]]
+        params["TranShkStd"] = params["T_cycle"] * [self.TranShkStd[0]]
+        params["Rfree"] = params["T_cycle"] * [self.Rfree]
+        params["UnempPrb"] = params["T_cycle"] * [self.UnempPrb]
+        params["IncUnemp"] = params["T_cycle"] * [self.IncUnemp]
+        params["wage"] = params["T_cycle"] * [self.wage[0]]
+        params["labor"] = params["T_cycle"] * [self.labor[0]]
+        params["tax_rate"] = params["T_cycle"] * [self.tax_rate[0]]
+        params["cycles"] = 1  # "finite horizon", sort of
+
+        # Create instance of a finite horizon agent
+        FinHorizonAgent = NewKeynesianConsumerType(**params)
+
+        # delete Rfree from time invariant list since it varies overtime
+        FinHorizonAgent.del_from_time_inv("Rfree")
+        # Add Rfree to time varying list to be able to introduce time varying interest rates
+        FinHorizonAgent.add_to_time_vary("Rfree")
+
+        # Set Terminal Solution as Steady State Solution
+        FinHorizonAgent.solution_terminal = deepcopy(self.solution[0])
+
+        dx = 0.0001  # Size of perturbation
+        # Period in which the change in the interest rate occurs (second to last period)
+        i = params["T_cycle"] - 1
+
+        FinHorizonAgent.IncShkDstn = params["T_cycle"] * [self.IncShkDstn[0]]
+
+        # If parameter is in time invariant list then add it to time vary list
+        FinHorizonAgent.del_from_time_inv(shk_param)
+        FinHorizonAgent.add_to_time_vary(shk_param)
+
+        # this condition is because some attributes are specified as lists while other as floats
+        if type(getattr(self, shk_param)) == list:
+            perturbed_list = (
+                (i) * [getattr(self, shk_param)[0]]
+                + [getattr(self, shk_param)[0] + dx]
+                + (params["T_cycle"] - i - 1) * [getattr(self, shk_param)[0]]
+            )  # Sequence of interest rates the agent faces
+        else:
+            perturbed_list = (
+                (i) * [getattr(self, shk_param)]
+                + [getattr(self, shk_param) + dx]
+                + (params["T_cycle"] - i - 1) * [getattr(self, shk_param)]
+            )  # Sequence of interest rates the agent faces
+        setattr(FinHorizonAgent, shk_param, perturbed_list)
+        self.parameters[shk_param] = perturbed_list
+
+        # Update income process if perturbed parameter enters the income shock distribution
+        FinHorizonAgent.update_income_process()
+
+        # Solve the "finite horizon" model, but don't re-solve the terminal period!
+        FinHorizonAgent.solve(presolve=False)
+
+        # Use Harmenberg Neutral Measure
+        FinHorizonAgent.neutral_measure = True
+        FinHorizonAgent.update_income_process()
+
+        # Calculate Transition Matrices
+        FinHorizonAgent.define_distribution_grid()
+        FinHorizonAgent.calc_transition_matrix()
+
+        # Normalized consumption Policy Grids across time
+        c_t = FinHorizonAgent.cPol_Grid
+        a_t = FinHorizonAgent.aPol_Grid
+
+        # Append steady state policy grid into list of policy grids as HARK does not provide the initial policy
+        c_t.append(self.c_ss)
+        a_t.append(self.a_ss)
+
+        # Fake News Algorithm begins below ( To find fake news algorithm See page 2388 of https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA17434  )
+
+        ##########
+        # STEP 1 # of fake news algorithm, As in the paper for Curly Y and Curly D. Here the policies are over assets and consumption so we denote them as curly C and curly D.
+        ##########
+        a_ss = self.aPol_Grid  # steady state Asset Policy
+        c_ss = self.cPol_Grid  # steady state Consumption Policy
+        tranmat_ss = self.tran_matrix  # Steady State Transition Matrix
+
+        # List of asset policies grids where households expect the shock to occur in the second to last Period
+        a_t = FinHorizonAgent.aPol_Grid
+        # add steady state assets to list as it does not get appended in calc_transition_matrix method
+        a_t.append(self.a_ss)
+
+        # List of consumption policies grids where households expect the shock to occur in the second to last Period
+        c_t = FinHorizonAgent.cPol_Grid
+        # add steady state consumption to list as it does not get appended in calc_transition_matrix method
+        c_t.append(self.c_ss)
+
+        da0_s = []  # Deviation of asset policy from steady state policy
+        dc0_s = []  # Deviation of Consumption policy from steady state policy
+        for i in range(T):
+            da0_s.append(a_t[T - i] - a_ss)
+            dc0_s.append(c_t[T - i] - c_ss)
+
+        da0_s = np.array(da0_s)
+        dc0_s = np.array(dc0_s)
+
+        # Steady state distribution of market resources (permanent income weighted distribution)
+        D_ss = self.vec_erg_dstn.T[0]
+        dA0_s = []
+        dC0_s = []
+        for i in range(T):
+            dA0_s.append(np.dot(da0_s[i], D_ss))
+            dC0_s.append(np.dot(dc0_s[i], D_ss))
+
+        dA0_s = np.array(dA0_s)
+        # This is equivalent to the curly Y scalar detailed in the first step of the algorithm
+        A_curl_s = dA0_s / dx
+
+        dC0_s = np.array(dC0_s)
+        C_curl_s = dC0_s / dx
+
+        # List of computed transition matrices for each period
+        tranmat_t = FinHorizonAgent.tran_matrix
+        tranmat_t.append(tranmat_ss)
+
+        # List of change in transition matrix relative to the steady state transition matrix
+        dlambda0_s = []
+        for i in range(T):
+            dlambda0_s.append(tranmat_t[T - i] - tranmat_ss)
+
+        dlambda0_s = np.array(dlambda0_s)
+
+        dD0_s = []
+        for i in range(T):
+            dD0_s.append(np.dot(dlambda0_s[i], D_ss))
+
+        dD0_s = np.array(dD0_s)
+        D_curl_s = dD0_s / dx  # Curly D in the sequence space jacobian
+
+        ########
+        # STEP2 # of fake news algorithm
+        ########
+
+        # Expectation Vectors
+        exp_vecs_a = []
+        exp_vecs_c = []
+
+        # First expectation vector is the steady state policy
+        exp_vec_a = a_ss
+        exp_vec_c = c_ss
+        for i in range(T):
+            exp_vecs_a.append(exp_vec_a)
+            exp_vec_a = np.dot(tranmat_ss.T, exp_vec_a)
+
+            exp_vecs_c.append(exp_vec_c)
+            exp_vec_c = np.dot(tranmat_ss.T, exp_vec_c)
+
+        # Turn expectation vectors into arrays
+        exp_vecs_a = np.array(exp_vecs_a)
+        exp_vecs_c = np.array(exp_vecs_c)
+
+        #########
+        # STEP3 # of the algorithm. In particular equation 26 of the published paper.
+        #########
+        # Fake news matrices
+        Curl_F_A = np.zeros((T, T))  # Fake news matrix for assets
+        Curl_F_C = np.zeros((T, T))  # Fake news matrix for consumption
+
+        # First row of Fake News Matrix
+        Curl_F_A[0] = A_curl_s
+        Curl_F_C[0] = C_curl_s
+
+        for i in range(T - 1):
+            for j in range(T):
+                Curl_F_A[i + 1][j] = np.dot(exp_vecs_a[i], D_curl_s[j])
+                Curl_F_C[i + 1][j] = np.dot(exp_vecs_c[i], D_curl_s[j])
+
+        ########
+        # STEP4 #  of the algorithm
+        ########
+
+        # Function to compute jacobian matrix from fake news matrix
+        def J_from_F(F):
+            J = F.copy()
+            for t in range(1, F.shape[0]):
+                J[1:, t] += J[:-1, t - 1]
+            return J
+
+        J_A = J_from_F(Curl_F_A)
+        J_C = J_from_F(Curl_F_C)
+
+        ########
+        # Additional step due to compute Zeroth Column of the Jacobian
+        ########
+
+        params = deepcopy(self.__dict__["parameters"])
+        params["T_cycle"] = 2  # Just need one transition matrix
+        params["LivPrb"] = params["T_cycle"] * [self.LivPrb[0]]
+        params["PermGroFac"] = params["T_cycle"] * [self.PermGroFac[0]]
+        params["PermShkStd"] = params["T_cycle"] * [self.PermShkStd[0]]
+        params["TranShkStd"] = params["T_cycle"] * [self.TranShkStd[0]]
+        params["Rfree"] = params["T_cycle"] * [self.Rfree]
+        params["UnempPrb"] = params["T_cycle"] * [self.UnempPrb]
+        params["IncUnemp"] = params["T_cycle"] * [self.IncUnemp]
+        params["IncShkDstn"] = params["T_cycle"] * [self.IncShkDstn[0]]
+        params["wage"] = params["T_cycle"] * [self.wage[0]]
+        params["labor"] = params["T_cycle"] * [self.labor[0]]
+        params["tax_rate"] = params["T_cycle"] * [self.tax_rate[0]]
+        params["cycles"] = 1  # Now it's "finite" horizon while things are changing
+
+        # Create instance of a finite horizon agent for calculation of zeroth
+        ZerothColAgent = NewKeynesianConsumerType(**params)
+        ZerothColAgent.solution_terminal = deepcopy(self.solution[0])
+
+        # If parameter is in time invariant list then add it to time vary list
+        ZerothColAgent.del_from_time_inv(shk_param)
+        ZerothColAgent.add_to_time_vary(shk_param)
+
+        # Update income process if perturbed parameter enters the income shock distribution
+        ZerothColAgent.update_income_process()
+
+        # Solve the "finite horizon" problem, but *don't* re-solve the "terminal period",
+        # because we're using the long run solution as the "terminal" solution here!
+        ZerothColAgent.solve(presolve=False)
+
+        # this condition is because some attributes are specified as lists while other as floats
+        if type(getattr(self, shk_param)) == list:
+            perturbed_list = [getattr(self, shk_param)[0] + dx] + (
+                params["T_cycle"] - 1
+            ) * [
+                getattr(self, shk_param)[0]
+            ]  # Sequence of interest rates the agent faces
+        else:
+            perturbed_list = [getattr(self, shk_param) + dx] + (
+                params["T_cycle"] - 1
+            ) * [getattr(self, shk_param)]
+            # Sequence of interest rates the agent
+
+        setattr(ZerothColAgent, shk_param, perturbed_list)  # Set attribute to agent
+        self.parameters[shk_param] = perturbed_list
+
+        # Use Harmenberg Neutral Measure
+        ZerothColAgent.neutral_measure = True
+        ZerothColAgent.update_income_process()
+
+        # Calculate Transition Matrices
+        ZerothColAgent.define_distribution_grid()
+        ZerothColAgent.calc_transition_matrix()
+
+        tranmat_t_zeroth_col = ZerothColAgent.tran_matrix
+        dstn_t_zeroth_col = self.vec_erg_dstn.T[0]
+
+        C_t_no_sim = np.zeros(T)
+        A_t_no_sim = np.zeros(T)
+
+        for i in range(T):
+            if i == 0:
+                dstn_t_zeroth_col = np.dot(tranmat_t_zeroth_col[i], dstn_t_zeroth_col)
+            else:
+                dstn_t_zeroth_col = np.dot(tranmat_ss, dstn_t_zeroth_col)
+
+            C_t_no_sim[i] = np.dot(self.cPol_Grid, dstn_t_zeroth_col)
+            A_t_no_sim[i] = np.dot(self.aPol_Grid, dstn_t_zeroth_col)
+
+        J_A.T[0] = (A_t_no_sim - self.A_ss) / dx
+        J_C.T[0] = (C_t_no_sim - self.C_ss) / dx
+
+        return J_C, J_A
diff --git a/HARK/core.py b/HARK/core.py
index 4d96241e2..42435b070 100644
--- a/HARK/core.py
+++ b/HARK/core.py
@@ -717,7 +717,7 @@ def unpack(self, parameter):
             self.__dict__[parameter].append(solution_t.__dict__[parameter])
         self.add_to_time_vary(parameter)
 
-    def solve(self, verbose=False, run_presolve=True):
+    def solve(self, verbose=False, presolve=True):
         """
         Solve the model for this instance of an agent type by backward induction.
         Loops through the sequence of one period problems, passing the solution
@@ -727,7 +727,7 @@ def solve(self, verbose=False, run_presolve=True):
         ----------
         verbose : bool, optional
             If True, solution progress is printed to screen. Default False.
-        run_presolve : bool, optional
+        presolve : bool, optional
             If True (default), the pre_solve method is run before solving.
 
         Returns
@@ -741,7 +741,7 @@ def solve(self, verbose=False, run_presolve=True):
         with np.errstate(
             divide="ignore", over="ignore", under="ignore", invalid="ignore"
         ):
-            if run_presolve:
+            if presolve:
                 self.pre_solve()  # Do pre-solution stuff
             self.solution = solve_agent(
                 self, verbose
diff --git a/examples/ConsIndShockModel/IndShockConsumerType_Jacobian_Example.ipynb b/examples/ConsIndShockModel/IndShockConsumerType_Jacobian_Example.ipynb
deleted file mode 100644
index cc0df25a2..000000000
--- a/examples/ConsIndShockModel/IndShockConsumerType_Jacobian_Example.ipynb
+++ /dev/null
@@ -1,417 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4ec2c3a0",
-   "metadata": {},
-   "source": [
-    "# Computing Heterogenous Agent Jacobians in HARK\n",
-    "\n",
-    "By William Du\n",
-    "\n",
-    "This notebook illustrates how to compute Heterogenous Agent Jacobian matrices in HARK.\n",
-    "\n",
-    "These matrices are a fundamental building building block to solving Heterogenous Agent New Keynesian Models with the sequence space jacobian methodology. For more information, see [Auclert, Rognlie, Bardoszy, and Straub (2021)](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA17434)\n",
-    "\n",
-    "For the IndShockConsumerType, Jacobians of Consumption and Saving can be computed with respect to the following parameters:\n",
-    "LivPrb, PermShkStd,TranShkStd,UnempPrb, Rfree, IncUnemp."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "37ca5c3e",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n",
-    "\n",
-    "\n",
-    "import time\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a6459df",
-   "metadata": {},
-   "source": [
-    "## Create Agent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c49cf2b0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Dictionary for Agent\n",
-    "\n",
-    "Dict = {\n",
-    "    # Solving Parameters\n",
-    "    \"aXtraMax\": 1000,\n",
-    "    \"aXtraCount\": 200,\n",
-    "    # Transition Matrix Simulations Parameters\n",
-    "    \"mMax\": 10000,\n",
-    "    \"mMin\": 1e-6,\n",
-    "    \"mCount\": 300,\n",
-    "    \"mFac\": 3,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "aff29a01",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "Agent = IndShockConsumerType(**Dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "88715dab",
-   "metadata": {},
-   "source": [
-    "## Compute Steady State"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "70f04d67",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPFRaw                 = 0.984539 \n",
-      "GPFNrm                 = 0.993777 \n",
-      "GPFAggLivPrb           = 0.964848 \n",
-      "Thorn = APF            = 0.994384 \n",
-      "PermGroFacAdj          = 1.000611 \n",
-      "uInvEpShkuInv          = 0.990704 \n",
-      "VAF                    = 0.932054 \n",
-      "WRPF                   = 0.213705 \n",
-      "DiscFacGPFNrmMax       = 0.972061 \n",
-      "DiscFacGPFAggLivPrbMax = 1.010600 \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Seconds to compute steady state 8.209359407424927\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "Agent.compute_steady_state()\n",
-    "print(\"Seconds to compute steady state\", time.time() - start)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e11268d",
-   "metadata": {},
-   "source": [
-    "## Compute Jacobians\n",
-    "\n",
-    "Shocks possible: LivPrb, PermShkStd,TranShkStd, DiscFac,UnempPrb, Rfree, IncUnemp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21b1f860",
-   "metadata": {},
-   "source": [
-    "### Shock to Standard Deviation to Permanent Income Shocks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e8ec958b",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Seconds to calculate Jacobian 9.14733076095581\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "\n",
-    "CJAC_Perm, AJAC_Perm = Agent.calc_jacobian(\"PermShkStd\", 300)\n",
-    "\n",
-    "print(\"Seconds to calculate Jacobian\", time.time() - start)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc78e4b8",
-   "metadata": {},
-   "source": [
-    "#### Consumption Jacobians"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "979e3529",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(CJAC_Perm.T[0])\n",
-    "plt.plot(CJAC_Perm.T[10])\n",
-    "plt.plot(CJAC_Perm.T[30])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a02ac39",
-   "metadata": {},
-   "source": [
-    "#### Asset Jacobians"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "201fbc06",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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bwBv7jOTvA1auppH9IkCJTdOM3A6eDaxERDQCw4hZsknVnSKMFGA1jWxaBUpzNY3KTc+IiCgNhhGzUk7TFCCMjLguDRCvkhRrGBFCpFjay03PiIgoNYYRs1I2sOb/2jQycKRqYC3aaZpwfE+W0ZuehSCEcGRYRERUnBhGzEq1tLeAO7Am9owU+zSNSAwjsmfEFz9vrI4QEVEihhGzUjWwugoQRuRqmoSeEVklKdalvVpC2Bg5TQMwjBARUTKGEbNSrqYpwHbwKXpGir4yEpJTWi4oLn23WlkhAdjESkREyRhGzHJomsboGSmpaZrkreABQFGUpL4RIiIiiWHELKMykqKBNY+NpEbPSMJxZTAp1gbWkVfslYyNz1gZISKiBAwjZhlhJLEyEvuwLUBlpJT2GTEqIyPDCCsjRESUAsOIWQ71jMjKSMqekSJtYI3vMeJJul/lXiNERJQCw4hZY+7AWtieETllE4wGi3LPDiOMeJLDiLHxGadpiIgoAcOIWWNeKC//PSOpKiOJjxcTuc+Immaahj0jRESUKKswsmnTJsyYMQMVFRVYtmwZXnrppbTP/eUvf4klS5agvr4eVVVVWLRoER555JGsB+yYVA2srsL1jKRaTQMUZxNrvDKSpmeEF8sjIqIElsPIli1bsHbtWmzYsAF79uzBwoULsWLFCnR2dqZ8fkNDAz7/+c9j165d2LdvH1avXo3Vq1fjt7/9bc6DLyhjmiZVZaTw16ZRoAAo8jAyojKiGmEkf+eLiIhKj+Uw8sADD+COO+7A6tWrMW/ePGzevBl+vx8PPfRQyudfffXVeP/7348LL7wQ5513Hj7+8Y9jwYIF2LlzZ86DL6gxG1jzvwNrYmVEURRUxI49nMcglC05TTOqZ6RCH7M2XHwBioiInGMpjIRCIezevRutra3xN1BVtLa2YteuXRlfL4TAtm3bcODAAVx55ZXWR+uklGEk/1ftTdUzAgAVriIOI+kqI7EwIoaHCj4mIiIqXm4rT+7u7kY0GkVzc3PS/c3Nzdi/f3/a1/X29mLy5MkIBoNwuVz41re+hfe+971pnx8MBhFMaHLs6+uzMsz8SLUDq6cy9lgep2lS7DMCAD63DwgW5zSNliaMsDJCRESpWAoj2aqpqcHevXsxMDCAbdu2Ye3atZg1axauvvrqlM9va2vDfffdV4ihmZeqMiLDSHgwb4eVPSOJO7AC8crIUKT4qgzpKyOxnhFWRoiIKIGlMNLY2AiXy4WOjo6k+zs6OtDS0pL2daqq4vzzzwcALFq0CG+++Sba2trShpF169Zh7dq1xs99fX2YOnWqlaHab6yeES0CRMPJK21skqpnBIDRM1KMlZH0PSN6eGNlhIiIElnqGfF6vVi8eDG2bdtm3KdpGrZt24bly5ebfh9N05KmYUby+Xyora1N+nJcysqIP/59OD//2k+1mgaIL+8tzp6R1NemYWWEiIhSsTxNs3btWqxatQpLlizB0qVLsXHjRgQCAaxevRoAcPvtt2Py5Mloa2sDoE+5LFmyBOeddx6CwSB+/etf45FHHsGDDz5o72+Sb2l3YFUACD2MVNgfmmRlZGTPiLGaJlqMYST1dvDsGSEiolQsh5GVK1eiq6sL69evR3t7OxYtWoStW7caTa3Hjh2DqsYLLoFAAHfddRfeeecdVFZWYu7cufjRj36ElStX2vdbFEKqHVgVRe8bCQ8CeerdyNQzUpyVkdSbnnE1DRERpZJVA+uaNWuwZs2alI9t37496ecvfelL+NKXvpTNYYpLqmkaIB5G8jRNU5KVEdkzwsoIERGZwGvTmJVqmgYA3HJFTWErI7JnpCgbWDPuM1J8AYqIiJzDMGLWWJURIH9hJM0+I5WxEFTM0zSjLpRnVEaKb8xEROQchhGzjDDiS77fI7eEz9M0TWwH1nSVkaKcpjF6RpLHzMoIERGlwjBilrED68jKSGx5b4F7Rop6aW849dJeVkaIiCgVhhGz0k3TyI3PCrzPiJymKcqekTB7RoiIyDyGEbPS9ozktzKS9to0scpIMW4Hr6VZ2qv4WBkhIqLRGEbMSnWhPCDvDazpekaM7eAjxVcZQZqlvWolKyNERDQaw4gZWhQQUf37dKtp8lChEELEw0iaa9MUYwMrKyNERGQFw4gZcooGKOjSXhlEgNE9I8W9A2uaa9PIykgwCCFEwcdFRETFiWHEjLHCSB4bWEMJxy2pq/amWdorKyOAHkiIiIgAhhFzImNVRvLXwJoYNHwj9jcp6spILGjIq/RKiT9rQ8XXeEtERM5gGDFDhgLVA6gjTpknf5URGUZ8Lh8URUl6zOcu3k3PtKA+JrmviKS43UCsWsLKCBERSQwjZqRb1gvEKyN5aGCVQWNkvwhQ5JWR2IXwFK9v1GNyrxFWRoiISGIYMSPdRfKAgvSMyOCRqKh7RtJM0wCAEruPlREiIpIYRswwUxnJQxiRVY+R/SKJ9xVjZUSLBY2R0zQAoPpYGSEiomQMI2aku0gekNelvbLqIasgieR28BERSVoC7DQRiQCRCABA9aWYpklY3ktERAQwjJgjqw8jd18F8rrpmQwjqXpGEqslxbQLqzYcH0uqyojCyggREY3AMGKGEUZGf7jmszIip2BS9YwkhpFiWlEjgvGxjNz0DEi4WB4rI0REFMMwYkY49gHrSRFG3PmfpknVM6IoSlGuqJEhQ/F6oYxcBo14tUQbKp4xExGRsxhGzHCoMmKEkVTTQyjOFTVymibVFA2Q2DPCMEJERDqGETPGDCP53/Qs1TQNkLCipginaVI1rwK8WB4REY3GMGKGDBqppmkSNz2z+eJvcvolVQMrEF9RU0zTNDJkZKyMMIwQEVEMw4gZcrXKWNM0Qku+oJ4NzFZGimk1jdEz4ksdoFgZISKikRhGzJDLdlOFEdnACtg+VZOpZ0TePxQtnmWyxu6rvjSVEbkDKxtYiYgohmHEjLEqIy4PoLj07/MURtJVRipdehAqpspIpgZWpUIfs8YGViIiimEYMWOsnhFFSVhRM2jrYcfaDh4oziv3ZmpgZWWEiIhGYhgxY6zKCJCwC6u9H7Bj7TMCFOeVezM1sMYrI8VTzSEiImcxjJgxVs8IkLeNz8zuM1JUlZHh9FfsTbxfcDt4IiKKYRgxw2xlpMA9I/L+YuoZESG5A2uafUZYGSEiohEYRswYq2cEyF8YiYw9TVOMq2niDaysjBARkTkMI2YYlZHK1I/n6cq9cvqllHpG4g2sGXpGuM8IERHFMIyYYWwHnzoU5HuaJl3PiD+2++uQzSEoFxmvTeOPhRFWRoiIKIZhxAwZRjxpKiNuZ5b2yu3giymMyG3e0zaw+vUApQ3Ze66IiKh0MYyYEc5QGfFW6bchez9gQ7Ht5dM1sPrd+gf7oM0hKBeZGlhlGBGB4hkzERE5i2HEDGOaJk1lxBu7WF4oYOthjZ6RNCGoMlapGYwUzwd7xgbWyvg0jbD5woJERFSaGEbMMMJImtU03mr9NmxvGMm06ZmsjBTnNE26npFYcBOCV+4lIiIADCPmZFraa0zT2BxGMiztLcZpGs24am+a1TSV8eqSNlg84yYiIucwjJiRadOzPISRiBZBREQAjHGhvCKcpjGu2ptmmkZRVSOQMIwQERHAMGJOpu3gPbJnZMC2Q8rmVWCMpb1FOE0jr8arpLlQHpCwomaweMZNRETOYRjJJBoBNL1CkbFnxMbKSOL1ZjIu7bV5f5NcyGvTjBlGjMqIvdNaRERUmhhGMknc3TRjz4h90w6yX8SjeqAqqf9nkpueRUQE4WjYtmPnIlMDK5CwvJcbnxERERhGMku8CF0Be0ZkZSRdvwgQr4wAxdM3kqmBFUicpimOMRMRkbMYRjKR/RiqB1BdqZ9jTNPY3zOSrl8E0KsmHtUDoHhW1GRqYAUStoRnGCEiIjCMZCYrI+m2ggfysulZpovkScV2fRqjMjLGNI3CBlYiIkrAMJKJbA4do0JhTNPYWJ2QPSNjTdMACXuNFME0jYhGgbDeu6J4vWmfx2kaIiJKxDCSibHHyFiVkYTVNJpmy2FlZcTrSv+hDsT7RophmiZxR9UxG1grGUaIiCiOYSSTiIXKCET8+TmSW8FXpGuajSmmvUbkFA1gcp8RXrmXiIjAMJKZXNqbblkvkFw1salvJNN1aaRi2oVVNq8qXi8UNf2flsodWImIKAHDSCbhDBfJAwBVBTz2Lu+13DNSBNM0WmyaZqzmVQBQq2L7jDCMEBERGEYyy3TFXsnmvUbM9owU0zSNURnxjT1mbgdPRESJGEYyMaZpxmhgBWwPI2Z7RopqmkbuvjrGhmcAeKE8IiJKwjCSiVEZGbt3Ix5G7Nn4TE7TZNxnpIgqI5q8Ls0YG54BiQ2szo+ZiIicxzCSidEz4kxlJGMDazEt7Q3Fdl/NUBlR/fq5YmWEiIgAhpHMzCztBWzf+MxsGJE7sBbDNI3RwDrGsl6A28ETEVEyhpFMzGwHD9g+TSOnXWTYSEdWRophmkZehVdOw6TDfUaIiCgRw0gmZraDB5J3YbWBnHapzDA9VFRLe2OVDrmPSDrycRFwfsxEROQ8hpFMzGwHDwAeey+WJysdmcJIMa2mkUt15TRMOokNrEKIvI+LiIiKG8NIJlZ7RmwOI7LykU5RraaJTdMoJqdpIETS9WyIiKg8MYxkYrpnxOZpmoi5aZpiWk0Tn6YZO4woCdM4bGIlIiKGkUxM94zkqTKSoYFVPl4clRFzPSOKqsY3PuNeI0REZY9hJBOzPSNeZ3pGjAbWIugZEYPmVtMkPkdjEysRUdljGMlEVkbGumovkDBNY8/SXrOraYylvWHnKwzGNE2GBlYg8cq99oQ3IiIqXVmFkU2bNmHGjBmoqKjAsmXL8NJLL6V97ne+8x1cccUVGDduHMaNG4fW1tYxn190wrEPS3lV3nRs3vTM6jRNREQQjoZtOXa2NJP7jCQ+R3Cahoio7FkOI1u2bMHatWuxYcMG7NmzBwsXLsSKFSvQ2dmZ8vnbt2/Hrbfeiueeew67du3C1KlTce211+LEiRM5D74gQrFw4c3wAWtjz0hUixpX7TVbGQGAQNjZKoOsjCgZekaAeBiJBlgZISIqd5bDyAMPPIA77rgDq1evxrx587B582b4/X489NBDKZ//4x//GHfddRcWLVqEuXPn4rvf/S40TcO2bdtyHnxByEpHpsqIx74dWGUQATIv7fWoHmPL+EDE4TAiKyMZVtMAgFoVuz4NwwgRUdmzFEZCoRB2796N1tbW+BuoKlpbW7Fr1y5T7zE4OIhwOIyGhgZrI3VK2GJlJJh7GJFTNAqUjNemAYBqj96vMmBTv0q2hOwZqTIRRmpqAADaAMMIEVG5c1t5cnd3N6LRKJqbm5Pub25uxv79+029x2c/+1lMmjQpKdCMFAwGEQwGjZ/7+vqsDNNecpomQ+8GfDWx5+ceCBKbVxVFyfj8am81Tg+fxkDY2TBidjt4AFCrY5WRgf68jomIiIpfQVfT3H///Xj00Ufx+OOPo6Ii/eqUtrY21NXVGV9Tp04t4CgTaFp8B9ZMYaSiVr+NhoBwbruKmm1elapiU0SO94wY0zSZw4irSq/maAPOBigiInKepTDS2NgIl8uFjo6OpPs7OjrQ0tIy5mu//vWv4/7778fTTz+NBQsWjPncdevWobe31/g6fvy4lWHaJ3FlTMZpmur498Hc/rVvdo8RqRimaYQQpreDBwC1Wh9zlGGEiKjsWQojXq8XixcvTmo+lc2oy5cvT/u6r371q/j3f/93bN26FUuWLMl4HJ/Ph9ra2qQvRySGkUzBQHUB3thUTTC3aSU5TZOpeVUywoiD0zQiFAKiUQAml/bWyMoIe0aIiMqdpZ4RAFi7di1WrVqFJUuWYOnSpdi4cSMCgQBWr14NALj99tsxefJktLW1AQC+8pWvYP369fjJT36CGTNmoL29HQBQXV2N6urqtMcpCnKZrscPqCZym68GCPXnHEYsV0a8zoeRxGvMmJqmif1vr/WzZ4SIqNxZDiMrV65EV1cX1q9fj/b2dixatAhbt241mlqPHTsGNeGD+8EHH0QoFMLf/u3fJr3Phg0b8IUvfCG30edb2GS/iFRRC/SfBIZzrIyYvEieJHtGHJ2mkXuM+HxQXK6MzzemaQKcpiEiKneWwwgArFmzBmvWrEn52Pbt25N+Pnr0aDaHKA5ml/VKckWNTT0jZhtY5TSNkw2sVppXAUCt4jQNERHpeG2asSRO05jhi/W2lOM0jYWt4AHAVcNpGiIi0jGMjCVsco8RyabKSNYNrA5O08ir7yomLpIHxKdpuLSXiIgYRsYiKyPeDFvBSzKM5NgzYrUyUgz7jGhDcsMzcwEq3jMSgBAib+MiIqLixzAyFquVkYo6/TbXpb2ygdVjLozUxJYUF8NqGrPTNGp1LLhFIhAJu+0SEVH5YRgZi9kr9ko+e/YZMRpYTU7TGKtpnNxnxGoDq78SiG11z74RIqLyxjAyFrNX7JWMBtby24FVG7TWwKqoqnHlXu7CSkRU3hhGxmKEEXOhwPYG1hK6No2cpjHbwAokNrFyeS8RUTljGBmL1WkaebG8Ajewyp6R4egwwlo4p2NnK77PiMlzhYTlvbxyLxFRWWMYGYusNJieprF30zOzYSSxghIIOVNlMFbTmJymAeIbn3GahoiovDGMjMVyA6u9q2nMNrB6VA8qXBUAnGtiNVbTmGxgBThNQ0REOoaRsWS96VmO0zRha5URwPm+EWE0sGYTRlgZISIqZwwjY7G66VlFwmqaHDbyMiojZkMQ4n0j/SFn+i+s7jMCsGeEiIh0DCNjsXrVXlkZEVo8yFgkhLDcMwI4XxmRDayKlWka9owQEREYRsYWtnihPI8fUFz691lO1YS1MKIiCsBaGDH2GnGqZySL1TTsGSEiIoBhZGxWG1gVJecVNXKPESC7yohTG59pg3qgsNYzoo+ZO7ASEZU3hpGxWG1gBeK7sGa514isbFS6K+FW3aZfV+11uDISq24Y15wxwVWjPzca4DQNEVE5YxgZi1EZMdnACiQ0seYWRuS0i1ny+Y71jMSqG7Ip1QzZM8JpGiKi8sYwMharPSNAzst75TRLldmN1mLk851YTSOiUWgBWRmxEEZkzwinaYiIyhrDSDrRMKBF9O/N9owAOV8sT1ZG5FJds+piG671hXLb4yQbclkvAKg1FqZp6vRzFe0r/JiJiKh4MIykk7g010plxLg+TW9Wh5WVDauVkVqvflxHwkissqF4vVC9XtOvc9XpAYphhIiovDGMpCObVxUX4DL/AYvKcfrtUE9Wh5U9H1YrI7Wxikxfjru/ZkPuE2JligYAXLX6mMXgIEQoZPu4iIioNDCMpJPYvKoo5l9nhJGzWR022wbWOq+D0zQyjFhoXtWfX2OcW1ZHiIjKF8NIOtk0rwJARb1+m2UYyXqaJlYZ6Q1mNz2UC2MlTZW1MKK4XEaPSbS38OMmIqLiwDCSjtUNzyRZGRnuyeqw2U7TJFZGRA7XxcmGMU1joXlVMvpGelkZISIqVwwj6cidTL3W/rWf6zSNrIxYnaaRlZGoiBZ8rxGtP7tpGiDeNxLt7bFzSEREVEIYRtKRS3PlUl2zcgwjMkhUWwxBFa4KeFW90bbQfSPyqrtWp2mAeGVEY88IEVHZYhhJxwgjVisj9fptlqtpsq2MKIriWN9ILtM0qtxrhD0jRERli2EkHTlN47P4AWv0jPQCWtTyYbNdTQM4t6LGmKapttZ0C7BnhIiIGEbSk5URqz0jcjUNRFYbn2U7TQM4t6JGLu11ZdPAWivDCCsjRETlimEkHWOaxuIHrNsbDzBZrKgxpmmyCCNOVUbim57lsJqmj2GEiKhcMYykk20YAbLea0QIEa+MZDFN41hlJLbPSHbTNOwZISIqdwwj6eQSRrJcUTMUGUJU6H0mWYURh65Pk8s0jSpX07BnhIiobDGMpJNtAyuQ9Yoa2bzqUlyodFdaPqzjq2ksXpsGYM8IERExjKSXbQMrkBBGrFVGBmIBqMpTBcXK9XBinFtNI6dpsggj9bxyLxFRuSvrMPLPP/gTLv33Z/DCoe7RDwZzqYxkd+VeWRmxuhW85NSVe3NbTRPvGSn0NvZERFQcyjqM9A1HcCYQQvdAisvXyw/0XMKIxdU0iZWRbDhRGdGCQYhwGECWlZFYzwgiEYjBQTuHRkREJaKsw8j4Kn379DMDwdEP5tQzkl0Day4bngHO9IzIqggUBWqV9RClVFZC8XgAsG+EiKhclXUYaZBhJJCqMpJDz0iWS3uNMJLNMeFQZUT2i1RVQVGt/zkpimKsqGHfCBFReSrrMCIrI6dHhpFIEIjG7itgZSTb69JIdT79Q30gPICIFsnqPayK9me/kkYyNj7rYWWEiKgclXUYkZWRs4MjwohsXgUK2sAqNzzLtoE18XWFqo5oAdm8mkMYqeXGZ0RE5ay8w0i1DwBwemQDq2xe9fgB1WX9jY0wcsbSy2RlJNsGVrfqNgJJTxZb0WcjakzT5BBGxunnK3rWWiWJiIjODWUdRsan6xnJpXkVAPwN+u3gGcDCclVZzci2MgIA4yvGAwBOD5/O+j2s0GJ9HmpsW/dsuMfr5ytypjBjJiKi4lLWYSRtA2suzasA4G/Ub7WwpSv3ylUw9b767I4LYFyFXmU4O1yYKkO0pwcA4K6vz/o9XOP0MBI9w8oIEVE5KuswMj6hZ0TTEioYuWx4BgCeCiC2zBaD5v+1b0sY8TkTRuSKmGzIykj0jLVpLSIiOjeUdRgZFwsjmgB6hsLxB3LZ8Ezy69MlCHSZfklPsAdAfFVMNhoq9Q/2M8OF+WCXK2BcuVRGGuQ0DcMIEVE5Kusw4nGpqK1wAwDOBBI2Psu1ZwQAqibotxbCiJ2VkcKFkR4AOYaRcayMEBGVs7IOIwAwPtWKGtkzUsAwogkNvSE9jORSGRlfqVdkCh5GbJimYWWEiKg8lX0YSdnEKntGsm1gBYAqOU1jrmdkIDwATWgAbOoZCRaoZ6TXvmma6NmzEJpmx7CIiKiEMIyk2oXVgcpIb2zVTaW7El6XN+vDOrWaJrdpmti+LNGosVSYiIjKR9mHkZR7jYRkGMmlMmItjNjRvAoADRWFa2AVQiRM09Rn/T6q1wu1Rg9+nKohIio/ZR9GUk/TyDCS/UZexl4jg92mni7DSC5TNEA8jPQEe4xpn3wRQ0MQIf285VIZAQBXQ2wXVoYRIqKywzAy1jRNTj0jsTASMBdG7GheBYD62BWDNaEZq3PyxbiWjMcDtcqf03u5G/QeG1ZGiIjKT9mHkfHVsY3PEsPIcKxvoSKHyogRRkz2jMSCQ503tzDiUT2o9erjzvdUTeJKGkVRcnovo4mVYYSIqOyUfRhpqNKX9nYPJOwzIrdwj1UZsiJ7RgZPAyZWiNg1TQMUrm8k3ryaW4ACAHdsmoaVESKi8lP2YWRCbJ+Rrv7EMNKj31bk8CErd2AVGjCUeWWLURnJcZoGKGAYsWFZr+SKTdNETzOMEBGVm7IPI821sU3PAiGEo7EKxlCPfltZn/0buzzxyoqJqRq7VtMAhVvea8dKGklWRqJnGUaIiMpN2YeRcX4v3Kre79DVHwTCw0A0ViXJZZoGsLS8146t4CVZGSlYGLFhmsZlNLDyyr1EROWm7MOIqipoqtGrI539wfgUjaLmtpoGSOgbybyixs6eEVkZOT1s/orB2Yie7QFg1zRNrDJy2tzqIyIiOneUfRgBgKbaCgBAR99wfIqmog5Qczw9ckXNgPnKiB3TNBMq9RDUPZTfD3Y7e0bcE/QxR7oYRoiIyg3DCJC6MpLrFA0AVDfrtwPtGZ9qaxjx6x/sXYPmrxicDTsukid5mpqM99RCoQzPJiKicwnDCIDmWGWks284YVlv7h+wqJ2o3/aPHUbCWhgDYf3ifHZM0zRV6h/sHYMdOb/XWOy4Lo2k1tVB8ep7vkQ6O3N+PyIiKh0MI0iojPQF7VlJI9XEwkjfyTGflrhTqtywLBeyMtI91J3XLeHtDCOKosAdq44wjBARlZeswsimTZswY8YMVFRUYNmyZXjppZfSPvf111/HBz7wAcyYMQOKomDjxo3ZjjVvZGWko3/Y3mkaGUb6T435tNNDeqNpQ0UDXKor58M2VjZCgYKoiOZ1r5HIaX3c7sZGW97P3axPazGMEBGVF8thZMuWLVi7di02bNiAPXv2YOHChVixYgU603yADA4OYtasWbj//vvR0tKS84DzYUJtQmXEzmmaLMKIHdyqG+Mr9aWy+eob0YaHofXr1/CxLYw0xZpYGUaIiMqK5TDywAMP4I477sDq1asxb948bN68GX6/Hw899FDK51922WX42te+hg9+8IPw+Xw5DzgfmmtiPSP9w/ZO08iekeFeIDSY9mlyCW5jpT0f6kB8RU3XUH7CSKRbH7Pi8UCtqbHlPT2cpiEiKkuWwkgoFMLu3bvR2toafwNVRWtrK3bt2mX74AqlKWEXVk1u3W7HNI2vFvDErmY7RnVELsG1M4w0+fPbxCr3A3FNaMz5InmS7BkJdzCMEBGVE0thpLu7G9FoFM2xuX2pubkZ7e2Zl6+aFQwG0dfXl/SVTw2xXViFAEIDMozYME2jKKamavIZRvI1TRPp1sfsbpxg23u6m9gzQkRUjopyNU1bWxvq6uqMr6lTp+b1eKqqYEJsRU0kEAsjdkzTAEDtJP12jOW9+QgjckVN52B+Ptjl5mR29YsA4GoaIqIyZSmMNDY2wuVyoaMjufTf0dFha3PqunXr0Nvba3wdP37ctvdOR+7CKuycpgGAmth5GWN5r2xglU2ndpB7jeSvZyQfYYQNrERE5chSGPF6vVi8eDG2bdtm3KdpGrZt24bly5fbNiifz4fa2tqkr3ybGAsjitzzw7YwknmaJi8NrPmujHTrIcfWMDJBD1BaIIDoQMC29yUiouLmtvqCtWvXYtWqVViyZAmWLl2KjRs3IhAIYPXq1QCA22+/HZMnT0ZbWxsAven1jTfeML4/ceIE9u7di+rqapx//vk2/iq5mTyuEgDgCcX6U+yaprHSM1Jhf89IvsJIVO4xMsG+Mbuqq6BWVUELBBDp7ISreqZt701ERMXLchhZuXIlurq6sH79erS3t2PRokXYunWr0dR67NgxqAkXmDt58iQuueQS4+evf/3r+PrXv46rrroK27dvz/03sMnk+kq4EIVPiy3BtasyIpf39qUOI+Fo2Lhir63TNLEwcmb4DMJaGB7VY9t7A/GeEdd4+8YM6BufhQ4fRqSzE75ZDCNEROXAchgBgDVr1mDNmjUpHxsZMGbMmAEhRDaHKajJ4ypRi4SpATtW0wAZKyNyisatuG25SJ5U76uHW3UjokXQNdiFSdWTbHtvID+raQC9iTV0+DAiHfatziIiouJWlKtpnDC5vhK1Sqwq4q0GXFnltNGM1TSnAG30dWKM3VcrG6Aq9v3PoSoqJlbpQejkwNjXxrFKCBEPIzZO0wCAZ7J+vsIn7R0zEREVL4aRmCnjKlEXq4wIu6oiAFAzCVBcQDQEDIz+134+lvVKshpyMmDvB7sWCEAMDwMA3DZP03gmTwYAhE6csPV9iYioeDGMxNRVetDi0SsjIY+Nq3dcbqBO/4BFz7FRD+djJY00uVo/7okBez/Yo7GqiFpVBdXvt/W9vbEwEmYYISIqGwwjMYqiYGZVEAAw6B5n75vXT9dvz7496qG8VkaqYpURm6dp8rHHiOQxwginaYiIygXDSIJpFUMAgD7V5n1NZBhJURmRYWR8hb3THQAwuUb/YLc9jHTpe4y48hlGTp2CiEZtf38iIio+DCMJJsWmac4Ie65Ca6ifpt/2jK6MtAf0PpJmf/Oox3KVr2macLu+A6+n2f4xu5uaALcbCIeN0ENEROc2hpEEE9wDAIDOSJW9bzxOVkbSh5GJ1RPtPSbi0zQdgQ5EtIht7ytXusiVL3ZSXC54Jurngn0jRETlgWEkwTj0AwDeCdnblBmvjIyeppErXeQyXDtN8E/Q9xoREVuv3hs+pY/ZPdH+MQOJfSMMI0RE5YBhJEGtpm8Ff3Swwt43lj0jve8AWrwPYjA8iN7YtXDyEUZURTWqI3ZO1URO6hu4efIWRmJ7jTCMEBGVBYaRBJURPRgcHvRhKGRj82RNC6B6AC2SdPVeOUVT46lBtbfavuMlyMdeI8Y0zST7p2kA7jVCRFRuGEYSuIbPAADOihocPW3jVWNVF1A/Vf8+YarmVECvMOSjX0Syu4lVGxxEtKcHQP7CCPcaISIqLwwjkhBQBvUNyM6IGhzttvkS9ilW1OSzX0SSlZF3+t+x5f3C7Xo1R62uhqvG5lVHMUbPyHF7xkxERMWNYUQK9unTKADOogaH7Q4j42bot2cOG3edGtArIy1VLfYeK8G0Wj0Evd03eiVPNuRmZPnqFwEA73S9xyZ88iS0UChvxyEiouLAMCLFqiIhtRJBeO2vjIyfrd92v2XcZSzrzWNlZGbtTADA0b6jtryfXEmTrykaQN9MTa2uBjQN4bftCVFERFS8GEakQb1fJFKhbwV/xO4w0hgLI6cPGnfJnhE5lZIPsjLSG+zF2eGzOb9f+JQ+Zvek/AUoRVHgnamHqOCRI3k7DhERFQeGESlWGYFf3+Lc1gZWIDmMxJb3Gg2seayMVLorjfc/0pv7B3tErqSZmL8ABQDemTMAAKHDDCNEROc6hhEpFka8tRMAAN0DIfQNh+17//rpgMsLRIaB3uOIalF0BPRt1fPZMwIAM2pnALBnqiac5z1GJF+sMhJiZYSI6JzHMCLFwoi7uhETanwAYG/fiOoCGs7Tv+8+iK6hLkREBG7FjQmVE+w7Tgoz6mYAAI72Hs35veRy23xsBZ/ImKY5yjBCRHSuYxiRjGma8ZjZqF+b5lDXgL3HkFM13X8xVrdMqZkCl+qy9zgjyMrIkb7cPti1YNDoGZErXvLFa1RGjkIIkddjERGRsxhGpEC3futvwJxmff+M/e399h7D6Bt5y6hSyKCQTzPrYitqcqyMhN5+GxACak0NXA0NNowsPe/06YCiQOvrQ/TMmbwei4iInMUwIsVW08A/HnNa9DBywPYwcoF+2/2W0b8xvTa/FQYgHkbe6X8HYS37PpjQ0aMAAO+MGVAUxY6hpaVWVBjLh9k3QkR0bmMYkQZlZWQ8LpwYq4ycsjmMyL1Gug7Ew0hd/sNIk78Jle5KREQEx/uOZ/0+oaP61JJc6ZJv3lmzAADBg4cKcjwiInIGw4g00KnfVjXhgtg0TXvfMHoGbdwBdEKsMhLoxNs9+k6shZimURUVs8fpQejA2QNZv4+sUHhnzLBjWBlVzNHP1/CB/QU5HhEROYNhRAp06bfVTaip8GDKuEoANveN+GqAhlkIAzgR22OkEGEEAOaOmwsA2H8m+w92OU3jK1AY8c29EAAQfJNhhIjoXMYwAgChABCKrZypbgIAzM1X30jLAhz3uKFBwO/2o7Gy0d73T2NOwxwAOVZGZM9IbKVLvlVcqAeo4b/8BULTCnJMIiIqPIYRID5F464EvNUAYDSx7m/vs/dYLRfjqMcDQN//I9+NoJIRRs5kF0aiPT2IntW3k/dOm2bbuMbinT4dis8HMTiI8LFjBTkmEREVHsMIkDBFMwGIhYO5LbUAgDftbmJtWYCjHjeAwqykkWbXz4YCBd1D3ege6rb8elkVcTc3Q62qsnl0qSluN3wXxPpG9nOqhojoXMUwAgAD+rbsqG427po3SYaRPoSjNk4RTFyAI7IyUjXZvvfNwO/xG+HnL2f+Yvn1wcOFbV6VKubGpmoYRoiIzlkMI0DSShpp5vgq1Fa4EYxo9i7xrW7GgQo/AOACpcK+9zVhbkOsifWs9Q/24f1vAgB8sRUuheKL9Y2wiZWI6NzFMAIkT9PEqKqChVPrAQB7j5+17VBhEcFBj37a5wzbfGXgDGTfyBun37D8WhkGKi6cZ+uYMqmIragZeuN1bgtPRHSOYhgBUk7TAMAl08YBAF453mPboQ73HEYYQLWmYUrHW7a9rxkLGhcAAP7c9WdLrxNCGNMkcoVLoVTMuxBwuxHt6kb4xMmCHpuIiAqDYQRImKZJvnruJbIycqzHtkPJfT7mBENQjr9o2/uaMb9xPlyKC+2BdrQH2k2/LnziBLT+figeD3znnZfHEY6mVlSgYp5ejRl6ZU9Bj01ERIXBMAIkbXiWSE7THO4OoHcw+2u6JJJh5MJQCDhzCBjosuV9zfB7/MZUzd7OvaZfN/yGPq3jmz0bSqz5tpD8l1wCABh65ZWCH5uIiPKPYQRIO03TUOXF9PF6s+krNvWNGJURX2yzswJXRxZNWAQA2Nu11/RrgrEpGl+Bp2ikylgYGXxlryPHJyKi/GIYAeLViRHTNACweLreN/LikdwvYy+EMDYdu3DCQv3OQoeRpkUArFZG9JU0hW5elWQYCR44gOhAYZt+iYgo/xhGggNAOPYBN2KaBgDefZ5ewXjhoPWNwkY63n8c/eF+eFQPZs3469idzlRG9p/Zj8HwYMbnCyEw/PrrAGLNpA7wNDfBM2kSoGkY3met+ZaIiIofw0gg1rzq8RtbwSd69/l6GHn1RC96h3LrG9nTqTdgXjT+InhmvFu/8+Qr+rVxCqSlqgUtVS2IiqgxnrGEjx9HpKsL8HiMRlInVC5eDAAI/LGw4Y2IiPKPYSRxiibFdWJa6iowa0IVNAH88fDpnA61u2M3AODS5kuBcTOB+mlANAQc3ZnT+1qhKAreNeldAIA/nPhDxucPvqyPufLii6FWFHaTtkRV79bHHNhZuHNFRESFwTAyEFviOqJ5NZFdUzV7OvRKxOLmxXrwOb9Vf+Dg73J6X6tkGHnh5AsZnzv48ssAAH+sMuGU6nfrlaThN95A5HRuoZCIiIoLw0h/LIzUTkz7FDlV8/u3sg8jXYNdONZ/DAoUo4nUqTBy+cTLoSoqDvcezrjfyODuWBhZ4mwYcU+YAF/sOjWBFzKHKCIiKh0MI32xXT1rxgoj4+F1qTjcHcDBzuyuU7O7U5/umNMwB7Ve/SJ8mHkloLqBM4eB04eyet9s1PnqcHHjxQDGnqqJdHUh/PYxQFGMFS1Oqr7irwBwqoaI6FzDMCIrIzUtaZ9SU+HBu84fDwD47esdWR1md3usX6Tp0vidvhpg2nL9+wJXR949SZ/2eP6d59M+R07R+ObMgau2tiDjGkvVu/UwMvD87yEiEYdHQ0REdmEY6T+l345RGQGAFRfpYWXra+a3UZeEEPj9id8DAJZNXJb84AXX6bevP2H5fXPxnmnvAQDsPLETA6GBlM8Z2L4DAFB1+eUFG9dY/Isvhau+HtGzZzH4pz85PRwiIrIJw4hRGRk7jLRe2AxF0Zf4nugZsnSIgz0HcWLgBLyqF5dPHPHBftHN+u2xF4DeE5beNxcXjLsAM2pnIKSF8Nzx50Y9LqJRDOzQw0j1e/66YOMai+LxoOa97wUA9P36Nw6PhoiI7MIwYjKMTKjx4bLpDQCAX/3Z2tVjd7yjf6gvm7gMfo8/+cG6KfGpmjeesPS+uVAUBdfN1KsyTx99etTjQ3v3ItrTA7WuDv5LLx31uFNq/+Z6AED/M89AhO25XhARETmrvMNIKAAEe/Xvx+gZkd5/6WQAwM9ePg4hhOnDbD++HQBw9dSrUz9h/gf029d+Yfo97bBi+goAwM6TO9Erz0PMwHN6taT6iiuguN0FHddY/JddBldDA6I9PQj88Y9OD4eIiGxQ3mFEVkU8VXozaQbvWzARlR4XDncFsOeYuQvndQ91Y1/XPgDAlVOuTP2keTcBigs4sRvoeN3U+9rh/HHn44JxFyCiRfCrQ78y7hdCoH/bswCA6r++umDjMUNxu1F7vV4d6fnZYw6PhoiI7FDmYUQ2r7ak3H11pJoKD25YoE/nbPnTcVOHeOrQUxAQWDBhAVqq0lRfqpuAC9+nf/+n75p6X7usnLMSALDlwBaj2jO8bx9CR45AqahA9VVXFXQ8Zoz7oD7m/mefRbjdekMxEREVlzIPI3LDs0mmX/LBy6YCAJ7cexLdA8ExnyuEwBMHnwAA3Hz+zWO/8WX/rN/+eQsw3Dv2c210w6wbUOWpwtG+o3ixXb/uS88vHwcA1Fz7XriqR1+vx2m+2bPhX7IEiEZZHSEiOgeUeRhJqIyYtHj6OCycUodgRMPDfzg65nNf634Nh3oPocJVgetmXDf2G8+4ApgwV7+C8Cs/Nj2eXFV5qnDjrBsBAD9640fQhofR9+tfAwDqb7mlYOOwatyHbgUAnP3Zz6ANDzs8GiIiykV5h5E+62FEURTcefV5AIAf7jqK/uH0Kzp+8ZbekHrN9GtQ483Qk6IowOV36t//YSMQGjQ9plz9/by/h6qo2PHODrz52Peg9ffDM3ky/EuXFmwMVtW0tsIzeTKi3d3o2bLF6eEQEVEOyjuMmNzwbKRr57Vg1oQq9A1H8L2dR1I+pyPQgf9z6P8AAP7ugr8z98YLP6RfyXegA3j5IUtjysX02um4YeYNUITA2e99HwBQ/3d/B0Ut3j8PxevF+I99FADQ/Z3vQhuytvcLEREVj+L9tCkEE1vBp6KqCta+9wIAwP/sOIxTvaM/CH/4xg8R1sK4tOlSXNpscp8Otxe48l/173c+AAyZW7Fjh48u/Cgue0vB+PYARFWlMQ1SzOpvvtmojpx5+GGnh0NERFkq7zAy4QJg4kKgfobll95w8UQsmT4OQ+EovvKb/UmPdQ9147G/6I2Vdyy4w9obL/wg0DgHGDwN/O4+y+PK1rTqqfjwy/pU0valldD8FQU7drYUjwcTPvEJAED35v9B6J13nB0QERFlpbzDyI3fAD76PDBlseWXKoqC9TfOg6IAT+w9iadfjy8x/d+7/zeGIkOYP36+cUE601we4H0P6N/v/j5w7EXLY8tGzy9+gXFvn8WQT8GPFvTi4dcfLshxc1X7vhvgv/xyiGAQ7fd90dJmdEREVBzKO4zkaMGUenzkilkAgHW/fBWd/cPY27nX6BVZt2wdFBP7l4wy46+ARbfp3//in4HBM3YNOaVoTw+6/lMPQAP/cAP6/Qq+tfdb+HPXn/N6XDsoioKW9fdC8XoR+P3vceYHP3B6SEREZBHDSI7WXnsB5rbU4HQghDse2YnP7/w8AOADsz+ABRMWZP/G17UB42YAvceAJ+4EtKg9Ax5BCIFT996LaE8PfLNn46/+/y/juhnXISIi+MyOz6B7qDsvx7WTb9YsNN3zWQBA538+gME9exweERERWcEwkiOf24VNt12KmgoXDkS+j2P9x9Dib8EnLv1Ebm9cUQf8fz8AXD7gL1uB//spIA9TEGcfeQT9z/wOiseDif/xH1C9XmxYvgHTaqbhVOAU7vrdXRgIDdh+XLuNu/VW1Fx3HRAO4/iddyH41ltOD4mIiExiGLHBeROqseKvXoGnbh+EUNESvANVntrc33jSIuCWbwNQ9P6RX3/G1gpJ32+fRsf9XwEANH32s6i8eD4AoNpbjW+1fgsNFQ1488yb+OjvPoqe4R7bjpsPiqJg0n98GZULF0Lr7cXbq/8Jw2++6fSwiIjIBIaRHAkh8M1XvonfnvgJACDSeTN+/1oVbvvuixm3izflopuBGzcCUIA/fQf42e22bBfft3UrTnz604Cmoe5vP4Bxt30o6fHptdPxrdZvodZbi31d+3D71ttxuOdwzsfNJ9Xvx9T/2QzfnDmIdnfj7X+4Hf2xqw8TEVHxYhjJQSAcwGd//1l8e9+3AQCfWvwpPHjT3aj2ufHSkTO4/hu/T1plk7XF/wj87UOAywvsfwrYfAVwdGdWbyUiEXT99yac+MQngXAYtX9zPSbed1/KRtuLxl+EH1z3AzT7m3Gk9wg++H8/iJ8d+Bk0oeX4C+WPq74e0x/5IfyXXQZtYADv3HkX2r/8H4gOBJweGhERpaGIElgL2dfXh7q6OvT29qK21obpjxwJIbDzxE58+cUv48TACbgVN+5dfi9uma1fy+Vg5wA+9qPdONip91pcdcEEfPraObh4Sl1uBz7+J+AX/wT0HNN/nv8B4Op1QONsUy8feu11tP/7FzH8530AgIZVt6PpX/8Viss15uu6h7pxz+/vwYun9GXG88fPx79c+i9YPnF5dquFCkALhdD59a/j7A8fAQC4m5vRuOZu1N98MxSPx+HRERGVB7Of31mFkU2bNuFrX/sa2tvbsXDhQnzzm9/E0jGuY/LYY4/h3nvvxdGjRzF79mx85Stfwd/8zd+YPl6xhBFNaPjDiT/g4dcfxkvtLwEAJldPxpf/6stY3Jy8V8lwOIpvbHsL33n+MCKafooXTq3Hbcum4X0LJsLvdWc3iKEeYNt9wMvfByAARQVm/TWw6EPA3BsAT2XS04UQGNq9G2d++Aj6n34aAKDW1KD5859D/c03mz5sVIvi0QOP4puvfBOBsF5lmD1uNm467ybcMOsGNFY2Zvf75NnA73ei/YtfRPj4cQCAe8IE1N30v1B3883wnX++w6MjIjq35S2MbNmyBbfffjs2b96MZcuWYePGjXjsscdw4MABNDU1jXr+Cy+8gCuvvBJtbW143/veh5/85Cf4yle+gj179mD+/Pm2/jL5MBgexN7Ovdh1ahd+e/S3OBXQr2fjUT24de6tuGvRXajyVKV9/dunA9j4u7fw1L6TCEf1U+11q1g6owFXzG7EslnjMae5BpXesasTo5z6M7D9fuDAr+P3+WqBmVci2nQZhvobMPDKYfQ/tx2Rjg79cUVB3f+6ERM++Ul4WqxtgS91D3Xje69+D4/95TEEo3pPjEtxYVHTIixtWYrLWi7DReMvgt/jz+r980EbHkbPli3o/s53Ee2OL1X2XXAB/JcvQ9WyZai85BK4GxocHCUR0bknb2Fk2bJluOyyy/Df//3fAABN0zB16lT8y7/8C+65555Rz1+5ciUCgQCeeuop477LL78cixYtwubNm239ZXIR0SLoHOzEiYETONxzGG/1vIUDZw7gtdOvIaJFjOdVe6pxy+xbcNuFt2FS9STT7989EMRjL7+DR/90DG+fTr4ir6oAMxurcH5TNSbWVWJSfQUm1VdiYl0F6io9qKnwoKbCjUqPC4qiQAsGofX3I9LdjfBfXkF4928QOfAywmcGMXzWg1B/ctVF8aqoWzIdDTe8C765FwE1k4DqCYC3BvDVAG6fftVgC3qDvfjt0d/iyYNPYl/3vlGPT66ejNn1szG5ZjKa/E36V2UTGv2NqPZUo9pTjUp3ZUGneUQohP4dO9D7+BMY2LEDiCavTHKNHw/f7NnwTp8Od3MTPM3NcDc1w93UBFdtDdTqaqh+f8ZpLSIi0uUljIRCIfj9fvz85z/HzQkl/lWrVqGnpwdPPvnkqNdMmzYNa9euxSdi1xABgA0bNuCJJ57An/+ceofPYDCIYDC+EqWvrw9Tp061PYz8/IurMHT8bQTCAQyHByGEgPHRKGB8X+X2o8nfhIn+iZhY1QKXosa3HRcCkGdQiPheIEIAEAnPi9/XPxxBV98wuvqD6BkMIRSJQhECqhDwaBF4tCjcSbcReLUI/JEgqsJD8JhY3uvya6hpGUL1lGFUNQehjvH5GVHcCKpVCLn8CKkV0BQ3oorbuE3+3gMNKoSiAFAgoKBDDeN1dwBvugPY7x5EjxpJf7AEigAq4EKlcMEHFS6hwAUVbihwQYn9rCT9DAAKFOhHh/EzYj8rCfeM+lnAeF7FoIbJx4OYdDyESceCGHfW3JgBIORREPYpCHlUaC5AUxVEY7eaS4Gmyu/129iwk28VRf+zUWDcjnoORr/23GHxFzrnfn+i4jP7o5/DJVe939b3NBtGLDUudHd3IxqNorm5Oen+5uZm7N+/P+Vr2tvbUz6/vT39KpO2tjbcd1/+LxJX/4fXcdHbZlZZBAAcAXAE/TYduzH2lYterx9dlePQWVmPLv84dFXW40jdRByqm4xeXxUmoxuz1XcwWzuBFnEWzcoZNCtn0YKzaFD6UKXogc8tInBHe1EVzX7J8IqE78+qKg56PTjk8aDd7UKH241OlwudLhdOu1wIqAo0Rf+gHUIUQ0p+dpcdkxdAPYCL9R99IRcmnwamdgk09Qg0DAAN/UBDv8C4AcAfBNyxRUTesIA3LFCF4l1VRERk1fEjrwE2hxGzsuyizK9169Zh7dq1xs+yMmK3mptvQk97J6q91ajx1sDv8UNRVRj/DFOU+PRF7F+z+t3y/tTPM6Ye5HMSfza+Tbg/9hzF7YLi8UDxepNu4fEgCBeGvZUIeCow4KlAyFOBsACqohomRzRMiAqEIxoui2oIa3qFRr/RbzUh0A7gpHE/ILQI3JFBuCMBeCIBuCMDcEeHoYoIXCIMVUSgahG4RET/XujfKyISqzIIvTYSq/jo/9bX31yBwBQITI1VhuRjiiYgNIEwohhCGMOIYFiJIogIohCIQCAKzbiNQuhfiv59rN4E/ej690ZhKvZ/hfxexL8Xikh4TgouAE361yD0r+Mjnq1GBDwhDd6gBk9I/1KjAqomYrcY/bMm9HMlz5eszsjKW4r75UCV2O+gpB10uSjTE1CmvzY5Z97sRY4d21IYaWxshMvlQodsiIzp6OhAS5qGyJaWFkvPBwCfzwefz2dlaFlZdue9eT+GXaqdHgAREVGeWNr0zOv1YvHixdi2bZtxn6Zp2LZtG5YvX57yNcuXL096PgA888wzaZ9PRERE5cXyNM3atWuxatUqLFmyBEuXLsXGjRsRCASwevVqAMDtt9+OyZMno62tDQDw8Y9/HFdddRX+8z//EzfccAMeffRRvPzyy/j2t79t729CREREJclyGFm5ciW6urqwfv16tLe3Y9GiRdi6davRpHrs2DGoarzg8q53vQs/+clP8G//9m/43Oc+h9mzZ+OJJ54wvccIERERndu4HTwRERHlhdnPb14oj4iIiBzFMEJERESOYhghIiIiRzGMEBERkaMYRoiIiMhRDCNERETkKIYRIiIichTDCBERETmKYYSIiIgcZXk7eCfITWL7+vocHgkRERGZJT+3M232XhJhpL+/HwAwdepUh0dCREREVvX396Ouri7t4yVxbRpN03Dy5EnU1NRAURTb3revrw9Tp07F8ePHec0bE3i+zOO5Mo/nyhqeL/N4rqzJx/kSQqC/vx+TJk1KuojuSCVRGVFVFVOmTMnb+9fW1vIP1QKeL/N4rszjubKG58s8nitr7D5fY1VEJDawEhERkaMYRoiIiMhRZR1GfD4fNmzYAJ/P5/RQSgLPl3k8V+bxXFnD82Uez5U1Tp6vkmhgJSIionNXWVdGiIiIyHkMI0REROQohhEiIiJyFMMIEREROaqsw8imTZswY8YMVFRUYNmyZXjppZecHpLjvvCFL0BRlKSvuXPnGo8PDw/j7rvvxvjx41FdXY0PfOAD6OjocHDEhfP888/jxhtvxKRJk6AoCp544omkx4UQWL9+PSZOnIjKykq0trbirbfeSnrOmTNncNttt6G2thb19fX48Ic/jIGBgQL+FoWT6Xz94z/+46i/teuuuy7pOeVyvtra2nDZZZehpqYGTU1NuPnmm3HgwIGk55j5b+/YsWO44YYb4Pf70dTUhM985jOIRCKF/FXyzsy5uvrqq0f9bX3sYx9Lek45nCsAePDBB7FgwQJjI7Ply5fjN7/5jfF4sfxdlW0Y2bJlC9auXYsNGzZgz549WLhwIVasWIHOzk6nh+a4iy66CKdOnTK+du7caTz2yU9+Er/61a/w2GOPYceOHTh58iRuueUWB0dbOIFAAAsXLsSmTZtSPv7Vr34V//Vf/4XNmzfjxRdfRFVVFVasWIHh4WHjObfddhtef/11PPPMM3jqqafw/PPP4yMf+UihfoWCynS+AOC6665L+lv76U9/mvR4uZyvHTt24O6778Yf//hHPPPMMwiHw7j22msRCASM52T6by8ajeKGG25AKBTCCy+8gB/84Ad4+OGHsX79eid+pbwxc64A4I477kj62/rqV79qPFYu5woApkyZgvvvvx+7d+/Gyy+/jPe85z246aab8PrrrwMoor8rUaaWLl0q7r77buPnaDQqJk2aJNra2hwclfM2bNggFi5cmPKxnp4e4fF4xGOPPWbc9+abbwoAYteuXQUaYXEAIB5//HHjZ03TREtLi/ja175m3NfT0yN8Pp/46U9/KoQQ4o033hAAxJ/+9CfjOb/5zW+EoijixIkTBRu7E0aeLyGEWLVqlbjpppvSvqacz1dnZ6cAIHbs2CGEMPff3q9//Wuhqqpob283nvPggw+K2tpaEQwGC/sLFNDIcyWEEFdddZX4+Mc/nvY15XqupHHjxonvfve7RfV3VZaVkVAohN27d6O1tdW4T1VVtLa2YteuXQ6OrDi89dZbmDRpEmbNmoXbbrsNx44dAwDs3r0b4XA46bzNnTsX06ZNK/vzduTIEbS3tyedm7q6Oixbtsw4N7t27UJ9fT2WLFliPKe1tRWqquLFF18s+JiLwfbt29HU1IQ5c+bgzjvvxOnTp43Hyvl89fb2AgAaGhoAmPtvb9euXbj44ovR3NxsPGfFihXo6+sz/hV8Lhp5rqQf//jHaGxsxPz587Fu3ToMDg4aj5XruYpGo3j00UcRCASwfPnyovq7KokL5dmtu7sb0Wg06eQCQHNzM/bv3+/QqIrDsmXL8PDDD2POnDk4deoU7rvvPlxxxRV47bXX0N7eDq/Xi/r6+qTXNDc3o7293ZkBFwn5+6f6m5KPtbe3o6mpKelxt9uNhoaGsjx/1113HW655RbMnDkThw4dwuc+9zlcf/312LVrF1wuV9meL03T8IlPfALvfve7MX/+fAAw9d9ee3t7yr8/+di5KNW5AoAPfehDmD59OiZNmoR9+/bhs5/9LA4cOIBf/vKXAMrvXL366qtYvnw5hoeHUV1djccffxzz5s3D3r17i+bvqizDCKV3/fXXG98vWLAAy5Ytw/Tp0/Gzn/0MlZWVDo6MzjUf/OAHje8vvvhiLFiwAOeddx62b9+Oa665xsGROevuu+/Ga6+9ltSrRamlO1eJfUUXX3wxJk6ciGuuuQaHDh3CeeedV+hhOm7OnDnYu3cvent78fOf/xyrVq3Cjh07nB5WkrKcpmlsbITL5RrVMdzR0YGWlhaHRlWc6uvrccEFF+DgwYNoaWlBKBRCT09P0nN43mD8/mP9TbW0tIxqkI5EIjhz5kzZnz8AmDVrFhobG3Hw4EEA5Xm+1qxZg6eeegrPPfccpkyZYtxv5r+9lpaWlH9/8rFzTbpzlcqyZcsAIOlvq5zOldfrxfnnn4/Fixejra0NCxcuxDe+8Y2i+rsqyzDi9XqxePFibNu2zbhP0zRs27YNy5cvd3BkxWdgYACHDh3CxIkTsXjxYng8nqTzduDAARw7dqzsz9vMmTPR0tKSdG76+vrw4osvGudm+fLl6Onpwe7du43nPPvss9A0zfh/luXsnXfewenTpzFx4kQA5XW+hBBYs2YNHn/8cTz77LOYOXNm0uNm/ttbvnw5Xn311aQA98wzz6C2thbz5s0rzC9SAJnOVSp79+4FgKS/rXI4V+lomoZgMFhcf1e2tcKWmEcffVT4fD7x8MMPizfeeEN85CMfEfX19Ukdw+XoU5/6lNi+fbs4cuSI+MMf/iBaW1tFY2Oj6OzsFEII8bGPfUxMmzZNPPvss+Lll18Wy5cvF8uXL3d41IXR398vXnnlFfHKK68IAOKBBx4Qr7zyinj77beFEELcf//9or6+Xjz55JNi37594qabbhIzZ84UQ0NDxntcd9114pJLLhEvvvii2Llzp5g9e7a49dZbnfqV8mqs89Xf3y8+/elPi127dokjR46I3/3ud+LSSy8Vs2fPFsPDw8Z7lMv5uvPOO0VdXZ3Yvn27OHXqlPE1ODhoPCfTf3uRSETMnz9fXHvttWLv3r1i69atYsKECWLdunVO/Ep5k+lcHTx4UHzxi18UL7/8sjhy5Ih48sknxaxZs8SVV15pvEe5nCshhLjnnnvEjh07xJEjR8S+ffvEPffcIxRFEU8//bQQonj+rso2jAghxDe/+U0xbdo04fV6xdKlS8Uf//hHp4fkuJUrV4qJEycKr9crJk+eLFauXCkOHjxoPD40NCTuuusuMW7cOOH3+8X73/9+cerUKQdHXDjPPfecADDqa9WqVUIIfXnvvffeK5qbm4XP5xPXXHONOHDgQNJ7nD59Wtx6662iurpa1NbWitWrV4v+/n4Hfpv8G+t8DQ4OimuvvVZMmDBBeDweMX36dHHHHXeM+sdAuZyvVOcJgPj+979vPMfMf3tHjx4V119/vaisrBSNjY3iU5/6lAiHwwX+bfIr07k6duyYuPLKK0VDQ4Pw+Xzi/PPPF5/5zGdEb29v0vuUw7kSQoh/+qd/EtOnTxder1dMmDBBXHPNNUYQEaJ4/q4UIYSwr85CREREZE1Z9owQERFR8WAYISIiIkcxjBAREZGjGEaIiIjIUQwjRERE5CiGESIiInIUwwgRERE5imGEiIiIHMUwQkRERI5iGCEiIiJHMYwQERGRoxhGiIiIyFH/D94uM9+qvz/3AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(AJAC_Perm.T[0])\n",
-    "plt.plot(AJAC_Perm.T[10])\n",
-    "plt.plot(AJAC_Perm.T[30])\n",
-    "plt.plot(AJAC_Perm.T[60])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ae3a774",
-   "metadata": {},
-   "source": [
-    "## Shock to Real Interest Rate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "318efe19",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "CJAC_Rfree, AJAC_Rfree = Agent.calc_jacobian(\"Rfree\", 300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f172f736",
-   "metadata": {},
-   "source": [
-    "#### Consumption Jacobians"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "20ae8342",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(CJAC_Rfree.T[0])\n",
-    "plt.plot(CJAC_Rfree.T[10])\n",
-    "plt.plot(CJAC_Rfree.T[30])\n",
-    "plt.plot(CJAC_Rfree.T[60])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22187e1b",
-   "metadata": {},
-   "source": [
-    "#### Asset Jacobians"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "344473d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Ol+qCy3h/24cRowqiut2sjBARUUIMI1aN6RkxGljz3LshKyMu1QVFUUqnMmIED8XtjtpNY+9jJiKiwmIYsSocdZE8IDL0rECVEdkrUjI9I9FhxC3PVSjSS0JERGWPYcQqGTrk8kyBhp7JMCKXZ0plN40ZRlwuqDKMgEs1REQUwTBilVwWkcPOCjT0TC7TyPkipTKB1Wxgja6MgGGEiIgiGEasig8jBRp6JpdjXMbyUKn0jGhRYQROJ6Dqv3Iad9QQEZGBYcSqMcs0hRl6NqZnRLV/z4jQNCC6MqIoUTtq7H21YSIiKhyGEavGVEYKNGckXHrLNHKJBoAZQuSOGsHr0xARkYFhxKoxPSMFmjOiJdlNY+cwEtUXYoYRt2vMfUREVN4YRqxKtkxToK29cjeN7Bmx826amDDi1OeyqG5OYSUiolgMI1YlW6Yp1NAzo4FVhpKgFoQQIq/vnSlzmcblgmI0rnIKKxERxWMYsarIlRHZMyIrI4B9m1jNUfAul3mbOYWVV+4lIiIDw4hV8RNYC3yhvPieEcC+fSPR01clVkaIiCgew4hVcjnGET+BtbCVEblMA9i3byRRGFHNMGLPYyYiosJjGLEq6TJNnsfBxw09UxTFDCZy26/dsDJCRETpYBixqkgNrPG7aQD776jRoq5LI0V6Rux5zEREVHgMI1YVa+hZXM8IEKmS2LaBNXoUvIGVESIiiscwYlXI+BB1Jhh6lscttvETWAH7X58m4TKNxwgj3E1DREQGhhGrxjSwyi22IrLTJg/iJ7BG/2zfMMLKCBERjY9hxKpkDaxAXvtGzJ4RR6T/QoYRu/aMRCojkWM2J7ByNw0RERkYRqxKNmcEyGvfSPzW3uifZT+J3XA3DRERpYNhxKr4ZRpVBeQOl3yGEa30dtOIYKrdNAwjRESkYxixKr6BFYgafJa/WSMhLQQgyW4am/eMqDGVEV61l4iIYjGMWGVWRqLDiPFzHkOBuUzjGLtMY9swYlZGuExDRETJMYxYZc4ZieoVKUBlJNXQM9uGkUTj4I1lGsGhZ0REZGAYsSrhMo0n9r48SLS11+5Dz7RUDaxBex4zEREVHsOIVfENrNE/57EykmromW0bWBOGEY6DJyKiWAwjVsnqhyNRZaQAu2mi54zY/kJ5xtCz6N00Zs+IPY+ZiIgKj2HEqnCKZZo8VigSVUZsP/QsmKAyYo6Dt+cxExFR4TGMWJVomaaAlZFE4+BtG0YSjIOX23wZRoiISGIYsSpRA6ujAGEkwTh4r8Mbc5/dJBoHr3j0Y9Y4Dp6IiAwMI1YlnDMiw8hI3t5WjnyPaWA13nc0nL/G2Wwk3NrrNbb2jtjzmImIqPAYRqzQwoDQ9J+jl2lcPv17ISoj6tjKyGged/Fkwwwj0Q2sPv1ccTcNERFJDCNWRIeNROPgg8N5e+tEE1i9xvvat2dEP2Y10dCzUXsGKCIiKjyGESuiP/RjKiMV+vdgfj5ghRCRZRrH2Dkjtq2MBMc2sCpeo2eEYYSIiAwMI1ZET1iNaiSFS1ZG8tMzEhIhCAj9raKXaYzKSGn1jMjR+SGIUKgYh0VERDbDMGJFOGrgmaJEbpeVkTw1sEYPNYtZpnHYe5lGSzRnRIYRANqoPY+biIgKi2HEikQXyQOiekbyU6GI3rqbaBy8bZdpzAbW6KFnkXMnRvO3+4iIiEpHWYeRP7zWif/ZfgAHj6fZeCobWKObV4GonpH8NLDKgWeqosKhOszbfU59Z4p9l2lkz0jUbhpFieobYWWEiIjKPIz8aOs7WPvoa3izcyC9JySaMQJEekbyVKEwd9Kose9rXigvj1uKs5GoMgJE7ajx2zNEERFRYZV1GPG59L/+aDCc3hNk70Z8GDEqFPmqjMidNNHTV4ESGHqWYDcNELWjhoPPiIgIZR5GvC59yWMk3TBiLtPE9YzIoWd57hmJr4yYyzShUQgh8vLe2Ug0Dh6I7KhhZYSIiIAyDyM+I4z4066MJLhIHhC1TJOn3TQJZowAkWUaAWH2ldhJoqFnACsjREQUq6zDiKyMjAa19J4g54zELZdEGljzE0YSTV8FInNGAPvtqBFCJJwzArAyQkREsRhGYGGZRm6xjV+myffWXm3sdWnknx2KUd2x26yRUAgwlo6ir00DcAorERHFyiiMbNiwATNmzIDX68WSJUvw4osvpnx8b28vVq5ciebmZng8Hpx66ql44oknMjrgXPJabmCNGnoWzZXnBlajcTY+jAD2nTUim1eBRA2svD4NERFFOK0+YdOmTVi9ejU2btyIJUuW4O6778bSpUuxZ88eNDQ0jHl8IBDARz7yETQ0NOChhx7ClClT8O6776Kuri4Xx5+VnDew5mtrr5Z4mQbQl2qGQ8O221Ejl2iARMs0xpV7GUaIiAgZhJG77roL1157LVasWAEA2LhxIx5//HHce++9uOmmm8Y8/t5778Xx48fx3HPPwWWU62fMmJHdUeeIz2rPSLI5I9Fbe4WIHRWfA7IykjCMyJHwNps1oskwoqpQnLG/ZqpZGbHXMRMRUXFYWqYJBALYuXMn2tvbIy+gqmhvb8f27dsTPue3v/0t2trasHLlSjQ2NmLevHn47ne/i3A4eTXC7/ejv78/5isfrC/TJJkzIisjQKR6kkNmZUQdG0bsOmskMn117DErHtkzwnHwRERkMYz09PQgHA6jsbEx5vbGxkZ0dnYmfM6+ffvw0EMPIRwO44knnsAtt9yCO++8E//2b/+W9H3Wr1+P2tpa86u1tdXKYaYtUhnJ0TINkJftvXI3TfzQMyBSGbFdz0iSnTQAoPqM3TSsjBAREQqwm0bTNDQ0NOAnP/kJFi5ciOXLl+Ob3/wmNm7cmPQ5a9asQV9fn/l18ODBvBybx2oYSdbA6nABqrEUkYftvWYYSdDAKrf32m03jZBX7HWNPWazMsKtvUREBIs9I/X19XA4HOjq6oq5vaurC01NTQmf09zcDJfLBYcjcoG3M844A52dnQgEAnAn+Jezx+OBx+MZc3uu+TJtYE3QuwGnDwgM5CWMJBt6BkR204zkaeBaplIu08ieEQ49IyIiWKyMuN1uLFy4EFu2bDFv0zQNW7ZsQVtbW8LnnHfeedi7dy80LdIk+tZbb6G5uTlhECkky0PPks0ZASJTWPMZRhL0jNi+MuIeWxkxd9OwMkJERMhgmWb16tW45557cP/99+ONN97A9ddfj6GhIXN3zdVXX401a9aYj7/++utx/Phx3HDDDXjrrbfw+OOP47vf/S5WrlyZu79FhnI2ZwTI6/beZBNYgajdNHYLI0lGwQPRc0bsdcxERFQclrf2Ll++HEePHsXatWvR2dmJBQsWYPPmzWZTa0dHB1Q1knFaW1vxhz/8ATfeeCPOPvtsTJkyBTfccAO+8Y1v5O5vkaGcNbACeb1yb6qeEfsu08iekQQNrNxNQ0REUSyHEQBYtWoVVq1alfC+rVu3jrmtra0Nzz//fCZvlVcZj4NPsKsln1fuHW/oGWDfygh30xAR0Xh4bRpk0DMSf9VeIGqZJvf/2pcDzeSSTDTbDj0zgoa8Dk0089o07BkhIiKUfRjR//pZj4MHoi6Wl/swIgeaeRK8r7zNdss0RtBQE+yKUjzcTUNERBFlHUZkz0ggpEHTxPhPSNnAWqF/z0MYkUswngQVGZ/Rq2K3ZRp53ZlElRHVx900REQUUdZhRC7TAIA/lMZSTarKSB639qZapjGv2mu3cfDGMo28Dk00szLCnhEiIgLDiPlzWks1ctuuc2woyGfPSMplGhlGbDYOXlY95LTVaKrsGeFVe4mICGUeRhyqArfDwqwRWfWIvhaN5Mzfbhq5BJOoMmLXZRphNrCODVAyjAiGESIiQpmHEQDwWGliTacykoc5I7LqkahnxK6VkUgDa/LdNMLvh9DS3MlEREQnrbIPI5YGn6WqjORxAqtZGUkQguRtdusZ0VJURqKXboTfXhUdIiIqvLIPI14rYcSsjBR2a2+q3TR2nTMil2DURLtpogIK+0aIiKjsw4jPyuAzM4wkqozkb2tvymUapz1305hbexMt0zidgEufYsu+ESIiKvswYuliebI51ZWoZyT/Q88SLtMYlRHb9YyYlZEEVSRwRw0REUWUfRjxpHt9Gk0D5I6VVJWRPI6DT7hMY9Nr02hGL0iiyggQdeVe9owQEZW9sg8jaS/TRFceElVGzJ6R3P5LP6SFEBIhAOMMPbNpZSRRAysQdeXeEXuNsSciosIr+zCS9vVpoj/sE1ZG8rO1N7rikWjomZwzEhIhhLRQTt87G7IykqiBFYi6ci8rI0REZa/sw4isjPjHCyOyF0R1Ag7n2PvztLU3uuKRas4IYK+lGrMykuBCefrt7BkhIiJd2YcRubV3JJBmZSRRVST69hw3sMqA4VbdUJWx/3N5HB4oUADY68q9chx80sqIHHzGZRoiorLHMCJ7RkJphpFE/SJA3pZpUl2XBgAURTGXakbysJMnU+Y4+GQNrJV6w682bJ9jJiKi4mAYSbeBVTamJhoFDwBuYzdNIMc9Iymu2CvJMDIcyv0o+kyNu7XXJ8OIfY6ZiIiKo+zDiC/drb1yCSRpGKmKPE5LY2ZJmlJNX5UqjG3F9lqmkePgkyzTVBhhhMs0RERlr+zDSNpDz1INPAMAd2XUY3P3r/1UA8+kCqf+wT6ch4v0ZUIEg0BYP59qkgZWM4wMDxXsuIiIyJ4YRtK9No1ZGUnWwOoFjEZSBHL3AZtq4Jlkt2UaLWq7btLKiE8/Zi7TEBFR2YeRtIeejVcZUZTIUk0Ow4jZwFpCyzTR15tJtrVXNRpYuZuGiIjKPox45NCzbLf2ApGlmlxWRoyekVTLNGZlxCbLNJq5k8YDRVESPsasjAzZ45iJiKh4yj6M+HK1tRfISxhJdcVeSfaM2KYy4pej4JOfK4UNrEREZCj7MJL20LPgOD0jQNT23jxURlJs7ZXLNLbpGZHbepMs0QDRDaz2OGYiIiqesg8jlZ50t/amUxkxekaCuQ8jyYaeAfZbphHjbOsFOGeEiIgiyj6MVLj168wM+dOtjHCZZjzCSmVkhGGEiKjclX0YqTTCyHBgnCvehsaZwAoArvwt0/hSLA/ZbmuvbGD1JT9mtYJbe4mISFf2YaTCWKYZDoShaSL5A2VlxJWqZyQPW3vTqYy4bDb0zJ9+ZURwNw0RUdkr+zAiKyPAOH0jxvCxQi/TWNnaa5dlGrMykqpnhLtpiIjIUPZhxOtSIUdhDKVaqgmlUxnJ/TJNWkPPnPbaTWNWRpJcJA+ILOGIQAAiNM4SGRERndTKPowoihLpG0nVxDreVXuBqGWawRwdXZrj4F12q4wYc0Y8KSojlZFr+bA6QkRU3so+jACAz633jWRfGTE+YHPYu5HOMo3tLpRnLtOkqIy4XIBDP+9sYiUiKm8MIwAq3ZEm1qTSqozkYWtvGss0tttNYzawppjAqiiRvhE2sRIRlTWGEURmjaQMI6E05oy4ZBjJ/TJNOhNY7bJMk05lBOCsESIi0jGMIDKFddifapnG2E2T1rVpcvfhalZG0pjAOhIagSbGufpwAWijeihKVRkBIhfLE1ymISIqawwjiJrCmnKZJp1r0+RvAmvKyojRMxL9+GISaWztBbi9l4iIdAwjiKqMpGxgLc5Ve81r06ToGfE6vVCg70+2Q99IOlt7AV4sj4iIdAwjSPP6NGYDazq7afLQwJpimUZVVHO3zUiw+FUGc+jZOMs0ihwJzwZWIqKyxjCC6N006WztLXBlJI0GVsBeg8/MC+WNWxnRzxeXaYiIyhvDCIAKzziVES0MhAP6z6kqI3I3TWgUCGc/VVQTGgKa/r6plmkAe42E1/zpVUZkAyuXaYiIyhvDCNKojEQ3haZTGQFyslQj+0WA1EPPAHtdLE9WRtLe2jucu0oSERGVHoYRpLGbJhQJBSnnjDg9gKIHm1xs743eGTNeZUQu09iiMmIu06SoIiHqyr1cpiEiKmsMI0hjzohsClVdgOpI/kKKktO+ERksPA4PnKoz5WPtNIVVDjFTKytSPk6t4DINERExjAAAfGZlZJxlmlTXpZHcuZvCKpdcoueIJGOnKazC2B0jKx/JRJZpin/MRERUPAwjSOPaNME0RsFLObxYnqxyyKCRilkZsUHPiKx0jBdGFDawEhERGEYARM8ZGa8yYiGM5GCZRoYRX6odPAa7bO0VgQBEMAggncqIsbWXYYSIqKwxjCB6AmuSyogMFq7KxPdHy+HF8kpxmSZ6ZojcupsMx8ETERHAMAIgjav2yqUPdxphJIcXy5PBwpdGr4oMLEM5nP6aCTNYuFxQ3O6UjzXDyBC39hIRlTOGEaRxbRpZGXGPX6HIaQNrKP3KSKVRkRkMZv++2Ui3XwQA1Koq/TmDxT1mIiIqLoYRRCojwbBAIKSNfYCVZRpPtf7d35/1cZnLNGk0sFa59Q/2oldG0txJAwCOKqNnhGGEiKisMYwAqHBHZockrI5YWabx1Ojf/QNZH5eVykiVSw8jgzmoyGTDUmWkWg9u2tAQhJYgBBIRUVlgGAHgcqhwO/VTkXAKq/yAT2eZxmuEkdHsKyOyZ8RSGCn6Mo1embGyTAOwb4SIqJwxjBjMWSOJtvfKZlR31dj74pnLNDmojBgVmXQaWCuNqk2xl2nkaPfxdtIA0BtcXS4AXKohIipnDCOGlNenMXtG0qiMmMs0ha2MVLv0EDQQyD4EZcPKMo2iKHAY1ZHwQHGPm4iIiodhxJDy+jSy2pBWz0juKyNWdtMMBYcghMj6vTNlJYwA0TtquExDRFSuMgojGzZswIwZM+D1erFkyRK8+OKLaT3vgQcegKIouPzyyzN527ySlZHBhMs0FsJIDntGrIyDr3brISgswhgNj47z6Pwxw8g4F8mTzDAyxGUaIqJyZTmMbNq0CatXr8a6deuwa9cuzJ8/H0uXLkV3d3fK5x04cABf+9rXcP7552d8sPlU5UlxsbxAcXbTWFmm8Tl9UKAAKO6OGnnRu3QrI3KZRuMyDRFR2bIcRu666y5ce+21WLFiBebOnYuNGzeioqIC9957b9LnhMNhXHXVVbjtttswa9asrA44X6q9ehgZGE1RGbHUM9KX9TGZDaxpXJtGURRb7KiRlREljQZWIFIZCbOBlYiobFkKI4FAADt37kR7e3vkBVQV7e3t2L59e9Lnffvb30ZDQwO++MUvpvU+fr8f/f39MV/5ljKMZNozkmXvhpVlGsAeg88iPSNpnCuwZ4SIiCyGkZ6eHoTDYTQ2Nsbc3tjYiM7OzoTPefbZZ/Gzn/0M99xzT9rvs379etTW1ppfra2tVg4zI1UefYtp/2hw7J1Wlmlkz4jQsr5yr5XKCBBpYi3mjhqrDayOahlGuExDRFSu8rqbZmBgAJ///Odxzz33oL6+Pu3nrVmzBn19febXwYMH83iUupwt07gqAMWY6Jpl34jVyohsYi1uZST9oWcAoFZymYaIqNw5rTy4vr4eDocDXV1dMbd3dXWhqalpzOPfeecdHDhwAMuWLTNv04yx306nE3v27MHs2bPHPM/j8cDj8Vg5tKylt0yTxtAzRdGXakZ7jVkjzRkdjyY0jIb0XTHpNLAC9qiMCIsNrOZI+AGGESKicmWpMuJ2u7Fw4UJs2bLFvE3TNGzZsgVtbW1jHn/66afjlVdewe7du82vyy67DB/+8Iexe/fugiy/pKvGqy/TDCRcprFw1V4gJztqRkOjENB7TtLuGXHZqGck7a29vFgeEVG5s1QZAYDVq1fjmmuuwaJFi7B48WLcfffdGBoawooVKwAAV199NaZMmYL169fD6/Vi3rx5Mc+vq6sDgDG3F1vSykg4CIQD+s/p9IwAkSbW0cx31MglGgUKvA5vWs+RDax22E2Tzjh4IGprL+eMEBGVLcthZPny5Th69CjWrl2Lzs5OLFiwAJs3bzabWjs6OqCqpTfYtTpZZSS6CdWVZhjxZl8ZGQnqyx0+pw+KoqT1HDtcudf6BFZjWBuXaYiIypblMAIAq1atwqpVqxLet3Xr1pTPve+++zJ5y7xLWhkxdrRAdQJOd3ovZm7vzXxLstXmVcAeV+61Hka4TENEVO5Kr4SRJ0nDiJVR8FIOekasTF+Vij1nRAgBzbhqr2J1AivDCBFR2WIYMchlmkF/CGEtaliZua3XShiRPSNZVEaC1isj5m6aYHF204hAAAjrVz1Oe+iZsZuGW3uJiMoXw4hBVkaAuIvlBS0MPJNy0DMil2nSHXgGANUuY85IlsPWMiWXaABA9aXXdCsnsIqREYhQgm3VRER00mMYMXhdDrgd+umIaWK1uq0XiOoZyX43jZVlmkojMBWrZ0QbMnYAeb1QHI60nuOojIQ8LtUQEZUnhpEosjoSUxkJWBh4Jnlq9e/ZVEYyWKaRlZGihRGL01cBQHG7oRgD7sK8Pg0RUVliGImSsInVyih4KQc9I7KB1coyjewZKdYyjRixNn1VUjlrhIiorDGMREk4a8TsGbHwAZvDnpFMdtMMBgehCS3j986U1W29krmjZoAXyyMiKkcMI1ESV0aMf61bWqbJwZwRecVeV/qVETlnRECYlZVCsjp9VZKVEe6oISIqTwwjUWQY6Y8JI0ZlxNIyTfaVETkrRAaMtN7W4YFTNQJVES6WJxtQZbhIV+RieayMEBGVI4aRKAmXaTIZeiaXaUZ6Mz6W/oBeVbESRhRFQa1bb57ty2InT6bC/XqYkOEiXY5a/ZjDfZlXkoiIqHQxjERJuEwTzCCM+Cbo30MjQMif0bHI68tUu619sNcYVRkZZgpJG9TDiCPjMNKb60MiIqISwDASJXFlJIOhZ55aAMbF7TKsjsjtuVbDSFErI8bF7qxXRvQAFe4r/DETEVHxMYxEqcnV1l5VBXx1+s8jJzI6FtnzYWWZBgBqPcULI7Lnw1Ft7ZhlZURjGCEiKksMI1FS76axUBkBIks1WYYRy5URI4wUY5kmbIQRtcraMavsGSEiKmsMI1ESLtPIHTFyh0y6sgwjcpmmysqWYgA1bv04i1kZUTOsjHCZhoioPDGMRElYGZGzQryFCyPBcBD+sN74mmkDa1+gCD0jsoG1xtq5ctQwjBARlTOGkSh1PjcAoHc4UWXEWijIJowMBCPzNiqd1paHZANrfxYD1zKlya29FueMOOqMMNLPZRoionLEMBKlrkJfpukdCURulNeXsbpM460znt9r+Thkv0ilqxIONb2r30q2qIxkvLW3D0KInB8XERHZG8NIFBlGRoMaRoNhIBzUZ4UABa2MyBkjVnfSAEWujGS8tde4ynEwCGGMlCciovLBMBKlyuOEU9Xng5wYDsSOcy9gA6tcprHaLwIUb2uvCAQgRkcBWK+MKD4f4NKDIPtGiIjKD8NIFEVRIks1w8FI86qrAnA4rb1YDioj2YSRQm/tjb7IndWeEUVRIks17BshIio7DCNx6ir0JtYTw4HM+0WA7CojGQ48AyJbeweDgwhqwXEenTvmtt6KCigOa30uQFTfSC8rI0RE5YZhJE6dT6+M9EVXRqz2iwC5CSMWZ4wAsdWUQl65N9NR8BJnjRARlS+GkTiRykgw0jNidcYIEBVGei0/VQ48k1UOK5yqE9UuPRAUsm/EvEheTbZhpDdXh0RERCWCYSSO7BmJXabJojIy2gdoYUtPzWaZBijOlXtlr4fVUfCSHJSmsWeEiKjsMIzEmWCEkb6R6GWaTCojdcYPQg8kFmSzTAMUZyR8ZFtvZsdsDj7jMg0RUdlhGIljLtMMBbILIw4XIPs3LPaNyGUaudxiVTG295rLNBlWRlQ2sBIRlS2GkTiRZZose0aAjPtGzKFnGVZGirG9lw2sRESUKYaROBOMykjfSJZbewHAZ0wWtVgZkSEikzkjQGSZppBTWLUB/b0ybmCt4ZwRIqJyxTASR27tjamMZNLACkQ1sfZaepq5TJNhGJGVkV6/tffNhlkZybSBVfaM9Pbm6pCIiKhEMIzEkT0jMRNYs12mGT5u6WnZXJsGAOo8dQAKG0bMoWcZN7Dq54phhIio/DCMxImMgw9AZDP0DAAq6vXvw8fSfooQIqtr0wDARO9EAMDxUWshKBuZXrFXck40wsixY7xyLxFRmWEYiSN7RkKagDaSZc9I5WT9+1B32k8ZDY8ipIUAlFYY0fplZSTDZZqJ+jGLYBBa1HVuiIjo5McwEsfndsDj1E+LkPNBMg4jRmVk6GjaT5EzRhyKAxXOiozetiiVkQE5gTWzc6X6fFAr9L9v+Fj6lSQiIip9DCMJyKWarLf2mpWRnrSfcmJU33lT66mFoigZva0MI72jvdCEltFrWBU+oR+37P3IhGPSJABA6Lj16/kQEVHpYhhJQF+qEXAEstxNY4aR9CsjclCZbELNhAwjIREqyMXyRDBoNrA6JtRl/DpOY6kmfJyVESKicsIwkkBdhQteBKAI45oyWfeMpB9GTvj1qkA2YcTlcJnTW4+N5v+D3RxUpigZL9MAkb6R0LHCLS8REVHxMYwkMKnKg2oM639QVMBdmdkLyZ6R0T4gFEjrKb3GTJJswggATPQZfSMj+f9gN5doamqgOBwZv45jEisjRETliGEkgclVHlQrI/ofPNVAhr0b8NYBqlP/eTi9vhE5G2SCN/PeC6CwTaxyNohjQnbH7Jxo9IywMkJEVFYYRhKor3JHKiOZLtEAgKpGZo2kuVQjw4icopopGUZkQ2w+hWRlJNswYlZGGEaIiMoJw0gC9VUe1ChGGPFmFwqs9o2YlRFPCVVGTvQCABx1dVm9jkNWRhhGiIjKCsNIAvVVHkyAMXjLl10oiMwaSW+Zxmxg9dZl9bYyjBSkgTVXyzSyMsI5I0REZYVhJIHJ1R7UKrkKIxYrIzlqYJU9J4WpjMgZI3VZvY65m4aVESKissIwkkB9tQd1RmVEFDqMGMs02YaRSV59yaOwDax1Wb2ODCPhEycgtMIMayMiouJjGElgUqUbdcoQAMDvyrZnxNoyTa7CSCEbWHNVGXHKZR5Ni8wuISKikx7DSAJelwP1Tj2MDKpZ7KYBLFVGguEghoL6+5bS1t5Qrx5GnFn2jCguFxy1evhj3wgRUflgGEmiwaHvpulDhgPPJAthRFZFVEXN+Iq9khx61uvvNa8CnC/mMk2WlREg6vo0nDVCRFQ2GEaSmKjqFYoTWq7CyPjLNHInTa27FqqS3f800a+R76Uac2tvlpURAHBM1F8jfCz9iwsSEVFpYxhJotZoYD0aqsjuhWTPyGAXIETKh5oXyctyWy8AOFSHuVTTPdKd9eslI0IhaP39+nvmIIw4J+vhLdidv2MmIiJ7YRhJokroV6HtCmYZRqqb9e/hADCceulBVjCybV6VGioaAADdQ/n7YI9uNM3mInmSq6ERABDqYhghIioXDCOJCIGKkB5GDge82b2W0x0ZCT9wOOVDc7WTRpJh5OhI+lcNtkrupFFra6E4nVm/nrPRCCOsjBARlQ2GkUT8/VARBgAcHPFk/3o1RnWk/0jKh+U6jDRW6B/sXcNdOXm9RCLNq1lugTY4G/UAFerK3zETEZG9MIwkMqL/a39EuHF4OMMr9karbtG/j1MZydsyzXD+qgyhHM0YkVxGZYQ9I0RE5YNhJBEjjPSiCt39o9m/XpqVEXkdmUm+Sdm/JwoTRuQ8EOek+py8nrlM09UFMU7DLxERnRwYRhKRYURUonvAj7CW5YdimpWRnhF9O2u9Lzcf7AWpjBgVDLkLJlvOBv2Yhd8PjVNYiYjKQkZhZMOGDZgxYwa8Xi+WLFmCF198Melj77nnHpx//vmYMGECJkyYgPb29pSPtwUjjPSjCmFNoHsgy+qIWRlJHUaODuuNppN9uflgL0TPSOiofsy5CiOqx2Mu+QS5o4aIqCxYDiObNm3C6tWrsW7dOuzatQvz58/H0qVL0Z1kjX/r1q244oor8Kc//Qnbt29Ha2srLr74Yhw6dCjrg88bYwvuqFPfqnqkL8swIisj4yzTmJWRitxURiZX6AFhIDCAkdBITl4zXqg7t2EEiFRHQt1sYiUiKgeWw8hdd92Fa6+9FitWrMDcuXOxceNGVFRU4N577034+F/84hf48pe/jAULFuD000/HT3/6U2iahi1btmR98Hkz0gsACHr0IV5HenNUGUmxTDMSGsFgUB+0lqvKSLWrGj6nD0D+lmpyXRkBYvtGiIjo5GcpjAQCAezcuRPt7e2RF1BVtLe3Y/v27Wm9xvDwMILBICYal4u3JWOZRhiTUI/0ZVlVkIPPRk4AwcSv1TOsV0W8Di+qXFXZvZ9BUZS8943kJ4zoxxxkGCEiKguWwkhPTw/C4TAajX+5So2Njejs7EzrNb7xjW+gpaUlJtDE8/v96O/vj/kqKCOMOCr1wJT1Mo1vAuA0hqcNJF6qkSPb6331UJQcbCc25DOMiHAYIbmbpiF3YcScwtqdv2FtRERkHwXdTXP77bfjgQcewMMPPwyvN/lk0/Xr16O2ttb8am1tLeBRwgwj7mp9i21ntmFEUSLVkSR9I3JKquzzyJV8hpHw8eOApgGKAuek3GxHBrhMQ0RUbiyFkfr6ejgcDnTFfUh0dXWhqakp5XP/8z//E7fffjuefPJJnH322Skfu2bNGvT19ZlfBw8etHKY2RvW/7VfWacHg8PZLtMAQM0U/XuSyohcpslVv4iUzzAil2gckyblZBS8ZC7TsIGViKgsWAojbrcbCxcujGk+lc2obW1tSZ93xx134Dvf+Q42b96MRYsWjfs+Ho8HNTU1MV8FZVxYrmqSvgsm6wZWIGp7b+JdRPmqjDRV6CHxyFDqnTyZyEe/CAC4mvVzFTqc+2MmIiL7sbxMs3r1atxzzz24//778cYbb+D666/H0NAQVqxYAQC4+uqrsWbNGvPx3/ve93DLLbfg3nvvxYwZM9DZ2YnOzk4MDg7m7m+Ra0N6lWLiZL2a0T0wilBYy+4166bp30+8m/DuXA88k6ZWTwUAHBrM/VbqSBjJ7TG7pujnPdzbi/DgUE5fm4iI7MdybX358uU4evQo1q5di87OTixYsACbN282m1o7OjqgqpGM8+Mf/xiBQACf+cxnYl5n3bp1uPXWW7M7+nwIDAMBPSjVTW6BUz2AkCbQPeBHS50v89etm65/700cRnI98ExqqdSrO4cG8hlGcnvMjqoqOGprEe7rQ/DQe3CcdlpOX5+IiOwlo4X+VatWYdWqVQnv27p1a8yfDxw4kMlbFM+QsYPD4YHDV4vGGi8O9Y7gSN9odmFkghFGklRGzGWaXIeRKj2MDAQH0OfvQ60nN1fXBfIXRgDANXWqHkbeew9ehhEiopMar00Tz1iiQeVkQFHQXKvv+jncm2UTq1kZ6dB3oMTJ9fRVqcJVgYlefYtyrpdq8h1GACD43ns5f20iIrIXhpF4sjJSqYeCqRP0asjBE8PZvW7tVEBxAGE/MBi7SyQYDqLX3wsg95URAJhalZ++kXyMgpdcU/W+kcB7Nr5sABER5QTDSDxjJw2q9O2l0ydVAgA6jmUZRhwuoNbY3nviQMxdcuCZS3XldBlFmlKlv2+u+0aCxvWIXMa1ZHLJzcoIEVHZYBiJZ1ZG9H/tT59UAQA4cCwHuzqSNLEeHtSvWdNS1QJVyf3/JFOq9TDy3mDuPthFMGgOJZO7X3KJyzREROWDYSTeYOIwknVlBEjaxGqGEWPnS66ZlZEcLtMEu7oATYPi8cBRn9s+FwBwTdHDSODQIQghcv76RERkHwwj8eIqI9Mm6ss0R/pH4Q+Fs3vtuhn69xSVkXzISxgxejlcLS05vZaO5JqinwsxPIzwiRM5f30iIrIPhpF4cWGkvsqNCrcDQgAHj2e5o2bCDP17XGVEhgQZGnJNNrAeHjycsypD8JARRvKwRAMAqscDp9GLwqUaIqKTG8NIPBlGqvQwoigKpk00lmqOZ9k3Yi7THIi5+fCQXhlprmrO7vWTaKpqgqqo8If95hbibOU7jADsGyEiKhcMI/HiKiMAMMPYUXOgJ8u+kQkz9e/9h4BgpMoil2nyVRlxqS6zH+VA/4GcvGYhwoh7uh7e/Pv35+09iIio+BhGomlh84q90WHEbGI9nmUYqawHvHUABHBsLwAgpIXQOdQJIH8NrAAwq24WAGB/X24+2M0w0pK/Y/bM1o85sI9hhIjoZMYwEm34OCCM6ahRk1CnGWHk3Wy39yoKMNkYbd7zFgCge7gbYRGGU3Xm/Iq90WbW6FWZfX37cvJ6gcOyMpK/MOKepYcR/77cHDMREdkTw0g0uUTjmwg4IpftmW7sqHk3F9t76+fo34/qYUQ2r7ZU5mfGiJTLyogIBhHqzN+MEcljhJHA/v0QCUboExHRyYFhJFrc9FVpdoMRRo4PZ7+9tz62MnJk6AiA/G3rlWbW5q4yYs4YcbvhzMOMEck1dSrgckGMjiJ4+Eje3oeIiIqLYSRav/GBV90Uc3NTjRfVXifCmsC+o1ku1cQt0+R7W68kl2k6hzoxHMyuwhM8pDfculpaoKj5+xVSnE64p08DAAT2c6mGiOhkxTASbUD/kEV1bJVCURSc2lgNAHirayC795DLND1vA1oY7w3o21bzXRmp89aZV+/NdkdN8GAHgPwu0UieWbMBAAH2jRARnbQYRqIlqYwAwKmNVQCAt7sGs3uPuumAw6Nfvbe3w+zhkMso+TSjZgaA7Jdq/MbuFtlgmk/uWfp58b/DMEJEdLJiGIk2YISRmrFVipxVRlSHWR0RR/eYwUAuo+STDDzZNrEG3nkHAOCZlf9jNptYWRkhIjppMYxEk2Gkeuwk1JyFEcAMI0c7d2EoOASH4sD0munZv+44ZtfpSx57T+zN6nXkVlu3sYSST+7Z+nv4336bF8wjIjpJMYxEk8s0NWPDyBxjmebd48MYDWa5o6ZxHgBgX+dLAIDW6la4HK7sXjMNp088HQDw5vE3M34Nze83x7PLoWT55DnlFMDpRLivD6Ej3FFDRHQyYhiRtDAwqM/OiG9gBYDJVR5MqHBBCGBvd5Z9I80LAAD7evUKxYzaGdm9XppOm6jv5Dk8dBi9o70ZvUbgwAFACKg1NXBMmpS7g0tC9XjgMaojo6+/nvf3IyKiwmMYkQa7AREGFDVmFLykKArmGEs1ezqzXKppPhsAsD9wAgAwqzb/FQYAqHHXmFfwffNEZtUR2bvhmTULiqLk7NhS8c6dCwAYff2NgrwfEREVFsOIJPtFqhpjpq9Gm9dSCwB45VBfdu9V1QBUN2O/S1+aKcROGumMSWcAAN48llkYkbtaCrGTRvKeoR/z6BsMI0REJyOGESlF86o0v1UPI397rzf792uej/0uPfQUqjICAGdM1D/YXz+e2ZJHYJ+xk6YA/SKS90xZGeEyDRHRyYhhROo3Bp4l2NYrnT21DgDw2uF+BMPZXStloPEMdDv1MFLIyki2TazFqIx4TjsdUBSEuroQOnasYO9LRESFwTAipVEZmTGpAjVeJwIhLeu+kTer9ebPJk1Btbs6q9eyQi7THOg7YHksvBYImNt6PXNOzfmxJeOoqoR7ur71mX0jREQnH4YRKcX0VUlRFLM6ku1SzesOfWbG3JFhwJ/l7hwL6n31aKhogIDAa8des/Rc/563gGAQjro6uKbkd3x9PO+ZZwIARl7+W0Hfl4iI8o9hREoxfTXa2VP1vpGXD2bXxPr6kH6BvDP9fuC9v2b1Wlad23AuAGBn105Lzxt9TQ8v3jPPLNhOGsm3UD/mkZ3WjpmIiOyPYUTq0wd5oSb1xd9yVhk5pjdjzg0EgI7tWb2WVec26h/sL3W/ZOl5o6+9CiBSpSikioWLAADDu/8GEQoV/P2JiCh/GEYAQAig76D+c920lA89d3odAGBP1wB6hwMZvd1gYNC8cu5cfwB497mMXidTsjKyu3s3Qlr6H+wjsjIyr/BhxDPnFKg1NRDDwxh9I/MJskREZD8MIwAwdBQIjQJQxq2MNFR7cUpDFYQAnt93PKO3e+O43oTZ7K3HRE0D3tsBhDILNpk4pe4UVLuqMRwaxlsn3krrOZrfD//b+sRYXxEqI4qqouKccwAAI7u4VENEdDJhGAGAXqMqUt0MON3jPrxtlr4TZvs7PRm9nblEM/lswDcBCI0AnS9n9FqZcKgOzG+YDwDY1bUrref434o0rzpbCtu8KvkWLgQADO9gGCEiOpkwjABA77v693GWaKS22UYY2ZfZzIuXj+rB48z6ecC0Nv3GA89m9FqZWtiof7D/tTO95tmRv+nH7J03r+DNq1LFIiOM/PWvEFp2c16IiMg+GEaAqH6R1rQe/n6jMvJW1yCODvgtvZUQAju6dgAwAsHMD+l37P2jpdfJVluLHoKeP/I8guHguI8ffuEFAEDFokV5Pa5UfGedBbWqCuHeXoy++mrRjoOIiHKLYQSILNPUphdGJla6cXqTPqjManVkb+9eHB89Dp/Th7PqzwJOvVi/o2M7MJrlNW8sOGPiGZjknYTh0DB2dadeqhGahuEXXwQAVCxZXIjDS0hxuVB53nkAgMFtzxTtOIiIKLcYRgCgt0P/nuYyDQB86FT9yr5b3uiy9FYvduof6gsmL4DL4QImzgImzQG0EPDOnyy9VjZURcUHp3wQAPDn9/6c8rH+t95CuK8PSkUFfPPmFeLwkqq64AIAwOAzDCNERCcLhhHA8jINAFx8ZiMA4Ok3uxEIpd+/IHs0FjdHVRjmGNWRt59M+3Vy4fyp5wMA/nwodRgxl2gWLoRiXGm4WCrP1wPU6CuvINSTWQMxERHZC8OIEFHLNOlXRha0TkB9lQcDoyG8sD+9pRpNaGa/yPua3he549SoMKKF0z6GbLW1tMGhOLCvbx86+juSPm7oBb2aU1nEJRrJ1dAA71z9Kr6D27YV+WiIiCgXGEZGTgAB46J3FiojDlXBR+Y2AACefC29pZpXe15Fn78Pla5KzJ00N3LHtA8A3jp93smB1FWKXKpx12Bxkx4wfr//9wkfo/n9kcrIkvcX7NhSqf5IOwCg//EninwkRESUCwwjcommcjLg8ll66sVz9YvqbX6tE6Hw+Es1Wzq2AADOn3I+XGrUcofTDZx5uf7zyw9aOoZsXTrrUgDA4/sfhxBizP1D27dDGxqCs7ER3jPnjrm/GGo+/nEAwNDzzyN09GiRj4aIiLLFMHLC2oyRaOedUo+JlW4cHfDjz2+n7l8QQuCP7+rbdy+aftHYB5z1Wf37G78FgqOWjyVTF027CG7Vjf19+/Hm8bFj1geeegoAUN3eDkW1x6+Lu7UVvvnzAU1D/+8TV3SIiKh02OPTpZhO7Ne/T5hp+alup4rLF+jj43+942DKx+7t3YuOgQ64VTfOn3L+2AdMawNqpgL+fuCtwn3AVrmr8KFWfdbJY/sei7lPhEIYfFrf4VP9kY8U7JjSIasjfb/9XZGPhIiIssUwctwIIxOthxEA+Oz7pgIA/vhGF44NJh+AJqsiH2j5ACpdlWMfoKrA2UZ1ZMe9GR1Lpi6bfRkA4JG9j2AkNGLePrxjJ8InTsBRV2dOP7WLmo9dAsXlwuirr2Lk5cKN0iciotxjGMmiMgIApzfV4OyptQiGBTYlqY5oQsOj7zwKAPjIjBQVhkX/CCgOYP8zQGfhJoyeP+V8TKmagv5APx7f97h5e+9v/g+AXhVRnM6CHU86nJMmoeZjlwAAjv/v/xb5aIiIKBsMI1lWRgDg6rYZAICf/+UARoNjt+Y+d/g5HBo8hGp3NT4yPUUYqWsFzlim//zCxoyPxyqH6sCVp18JAPjFG7+AEAKhEycwsPkP+mF99rMFOxYrJnzu8wCA/t9vRrC7u8hHQ0REmSrvMBIOAn3v6T9nWBkBgMvmt6Cl1oujA378ZtehMfc/uEffIXPZ7Mvgc46zY+f91+vfX/515NgK4PI5l8Pn9GFv715se28b+h55FCIQgHfuXPjOKu7U1WR8Z82D75xzgGAQx++7v9iHQ0REGSrvMNLbAYgw4PQB1U0Zv4zbqeKL588CAGzc9g78oUh15PDgYWx7Tx/O9fen/v34L9a6RJ87EvYD276X8TFZVeOuMasjP9zx3zjxq18BAOqWLy/YMWSi/rp/BgCc+MUvEOyyNpqfiIjsobzDiFyimTADUJSsXuqKxa2YXO1Bx/Fh3P/cAfP2n7z8E4RFGO9vfj9m180e/4UUBWhfp//80i+AnrezOi4rVsxbgWpXNRr/8haCHR1w1NWh9uOXFuz9M1F5wQXwLVwI4fejZ8OPin04RESUgfIOIyey7xeRKtxOfH3paQCAH2zZi55BPw72H8Qjex8BAKxcsDL9F5v2fuDUS/SqzRNf10fWF0Ctpxb/ePrV+Myz+gC3yi98Dmplgp0/NqIoChpW3wgA6H3oIe6sISIqQeUdRo5nt5Mm3mfOnYqzptRiwB/CLY+8iu+/9H2ERRjnTTkPCxoWWHuxpf8OOL3Avj8Bu3+Zk+NLx2Wv+dDUC/RVAD8/vbNg75uNioULUXPZMkDTcORbt0AEAsU+JCIisqC8w0gOKyMAoKoK1n/qLDhVBU8e2IrNBzZDVVTccM4N1l9s0mzgwjX6z39YAxzfl5NjTCV45AhO3P0DAMCDH1Sx6d1HsPXg1ry/by40rlkDx4QJ8L/1Frr/+7+LfThERGRBeYeRHFdGAGDelFp8+aIWeJseBgBc0vpZnDHpjMxerG0VMHUxMNoHbPo8EBjO2XHGE5qGI7feCm1oCL4FC9Bwpb5t9uY/35zyir524ZwwAU233goAOP6ze9H/hyeLe0BERJS28g4jy/8XuPLXwJRzc/aSYS2Mt7T/B9XVBy0wEU8/vwCHekfGf2IiDifw2fv1i/h1vQo8+AUglJ8liJ6NGzG07RkoLheav/NtrH7fV7Fg8gIMBAdw/R+vx9Fh+1+QrmbpxZj4hS8AAA7fdBOGd+4s7gEREVFayjuM1J8CnLoUqJiYk5cTQuA/d/wn/nL4WXgcXkwevhbdfcBnN25Hx7EMqxo1LXpocvqAt/8APLQi5xfS63v8cfT84IcAgKZb18EzZw5cDhfuuvAuTKmago6BDlz75LXoGrL/1tmGr30VlRecDzEygoNf+mcGEiKiElDeYSSHNKHhjr/egf99Qx9N/u0P3IZffeHTmFlfiUO9I/jUj/+CF/Ydy+zFp70f+IdfAA438OZjwP3LgIHcBIO+3/0Oh7/+r4AQmHDVVaj79KfN+yZXTMY9F9+DhooGvNP3Dq584kq8duy1nLxvvihOJ6Z+//uoWLwY2tAQOr6wAn2//W2xD4uIiFJgGMmB/kA/bnj6BjOIrG1bi4/N+hiaa33Y9M/vxxnNNegZDODKn76Au556K2YoWtpOuQj43P8B3lrgvReBH7cBb/wu422/IhRC95136UFE01D7mU+j8Zs3j3lca3Ur/ueS/8Gs2lnoHu7G5574HH72ys8Q1IIZvW8hqF4vWv/fRlR/pB0iGMThf/0GDn/jGwj39hb70IiIKAFFiAINschCf38/amtr0dfXh5qammIfjkkIgS0dW7D+hfXoHumGW3Xj1g/cimWzl8U8biQQxprfvIxHdh8GAMysr8RXLz4Vl8xrhkO1OGyt5229d6TLuJDe7L8DLloLtJyT9kuMvPIKOtfditHXXwcATFyxAg1f/xoUNXk27Q/0Y+1f1mJLxxYAwKzaWbjh3BtwYeuFUBV7Zlqhaej54Qb0bNwIaBoctbWoX/ll1H32s1C93mIfHhHRSS/dz++MwsiGDRvwH//xH+js7MT8+fPxgx/8AIsXL076+AcffBC33HILDhw4gDlz5uB73/sePvaxj6X9fnYLIyEthK0Ht+LeV+/FKz2vAACmVU/DHRfcgTPrz0z4HCEEnnilE+t++xp6Bv0AgCl1Pvz9oqn47KJWtNSNc82amAMwRsU/9wMgbDS0zjgfOHu5fqE9X13C9x9+8a84fv/9GHz6aQCAWlODpnVrUXtpelNWhRB4ZO8juGvnXej19wLQQ8llsy/Dx2Z+DM1Vzen/HQpoeNcudK5bB//bewEAjokTUXvZZahZ9nF4586FkuX0XSIiSixvYWTTpk24+uqrsXHjRixZsgR33303HnzwQezZswcNDQ1jHv/cc8/hggsuwPr16/Hxj38cv/zlL/G9730Pu3btwrx56V2AzQ5hpM/fh11du/CXw3/Blo4t6BnpAQB4HV58fu7n8aWzvwSvc/x/bQ/6Q/jpn/fh5385gL4RfalDUYB5LbU475R6fPCUesybUoO6Cvf4B3V8H7D1e8ArD+rTWgHA4QFmXQhM/wDCk8/B8MEABv/yHIa2PYPgYb0yA1VF7bJlaPj61+Csr8/oXPz81Z/jgT0PYCg4ZN6+YPICvK/pfVjUuAjzG+aj0mWf6a0iFELvQ/+HY/fcg+ChyMUM3TNnovK881CxaCEqFi6Ec/LkIh4lEdHJJW9hZMmSJXjf+96HH/5Q332haRpaW1vxL//yL7jpppvGPH758uUYGhrCY489Zt72/ve/HwsWLMDGjRtz+pfJVkgL4fjocRwdOYrOwU7s69uHfX378ObxN7G3d2/MYyd6J+KTp3wSn5v7OdT7rH+gjwbD+MNrnfjlCx14Yf/xMfc313pxWlM1pk7wobnWh+ZaL5pqvaiv8qDa60Sl24EKLQQxOIDwwT0I7XgYwZe3IdjZjUC/E6O9LgQHHQAi/+pXPU7UtJ2OiZ+8CJ7TztIvDuitBdyV+rRXixWCgcAAnjzwJB7f/zh2dO6AQOyvUnNlM2bVzcKMmhloqGjAZN9kTK6YjMm+yaj11KLCWQGf01fQyoQIBjH4zDPoe+wxDD79Jwi/P+Z+x4QJ8MyeDfesWXA1N8E5eTKcDQ1wTp4Mx4QJUCsroVZUQHE4CnbMRESlKi9hJBAIoKKiAg899BAuv/xy8/ZrrrkGvb29ePTRR8c8Z9q0aVi9ejW+8pWvmLetW7cOjzzyCP72t78lfB+/3w9/1IdEf38/Wltbcx5GHvm3L2L04LsYCY1gJDiCQNgfaQgV+se4+TEpgBp3Nep99ZhS0YKGislQ5L0C+vPM5wrjRn1pw7wfcY+DgBAC/kAYPYN+9Az6cWLQj5FAGA6hwaWF4A4H4dZCcGkhuML6d084iKrgCNxaaNy/o6syhMpmP6paRlHZEIDqTPw/dxgO+FUf/I4KBNQKhBUXwooTYcUJTXFAM392QlOd0OCEUBQI4ywdU0J42TWIN53D2OMcRo+aXoOrIgAvHPAKFR444BQqHFDghAIHFDiE8T3qz4px5uX/NpEoE7ldifsz4m8TgMuvYdoBP5oPBdD8XgCTjgaRbiwKOhUE3caXS4GmKtBU6N8dUT8b34XRViOU2O9QFDPCiagDFTHf9TtE3H2UoaTnjieVytucf74Z53zokzl9zXTDiNPKi/b09CAcDqOxsTHm9sbGRrz55psJn9PZ2Znw8Z2dya97sn79etx2221WDi0jlX/+G057d2j8B5r6ja996M/xsdQYX1aFFRUDLh96PVXorpiAo74JOFI5Ce/UtmB/bTN8ngBOUQ9jjvIepqIHDeETaFB60YATmKz0oUrRZ5Y4EEaFNogKbTDjv8PfRf3cq6rY53Jhn9uJDqcLPQ4Hup0O9DhUdDucGFQVPcwowAjCGFHCAAq8Q8cN4CzjC4An4EDLcWBKj0DLcYEJg8CEAWDCkMCEAaBqFHDq1xCEKyTgCgkgf0NxiYgK6uD+V4Ech5F0WQojhbJmzRqsXr3a/LOsjORaxWWXoqezCz5XBSpdlahwVcLr8um7Q+TSgaIY/9Q2/l2tKMbP0fcrUX/M7n4oiv4YhwOqxwPF7Ybi8UBxuaF43FDdbsDtRqiyGsOeCgyqboT9YSjBMKrDAtPCGoJhDcGwQEjTEAhpCGkCwbCGUFigG0CXkFUbACIER2gEztAwnKFBuELDcIWHoWoBKFoIqghB0YLmz6r8LkJQoBkVpEilR4Fe+ZH/lm+EQFPcbYoQEGEBvwjBjzD8CGIUIYwqIYQgEIaGMITxFfWzov+syaqT+a4i6md5S+zPiLpfxD3f5AAwWf8agv71XvT9QkANC7gCGlwB+V2DM6hB0QBV0+9XNQFVg/mzvE++qWK+uYj6Wa/WSIpxf+zjYx8Ty/ab4myO5y8tPE0ntblzFhTtvS2Fkfr6ejgcDnR1xQ7c6urqQlNTU8LnNDU1WXo8AHg8Hng8HiuHlpHzVua/+pJP1QAax30UERGRvVkaEOF2u7Fw4UJs2bLFvE3TNGzZsgVtbW0Jn9PW1hbzeAB46qmnkj6eiIiIyovlZZrVq1fjmmuuwaJFi7B48WLcfffdGBoawooVKwAAV199NaZMmYL169cDAG644QZ86EMfwp133olLL70UDzzwAHbs2IGf/OQnuf2bEBERUUmyHEaWL1+Oo0ePYu3atejs7MSCBQuwefNms0m1o6MDatQkzw984AP45S9/iW9961u4+eabMWfOHDzyyCNpzxghIiKikxvHwRMREVFepPv5bc+LihAREVHZYBghIiKiomIYISIioqJiGCEiIqKiYhghIiKiomIYISIioqJiGCEiIqKiYhghIiKiomIYISIioqKyPA6+GOSQ2P7+/iIfCREREaVLfm6PN+y9JMLIwMAAAKC1tbXIR0JERERWDQwMoLa2Nun9JXFtGk3TcPjwYVRXV0NRlJy9bn9/P1pbW3Hw4EFe8yYNPF/p47lKH8+VNTxf6eO5siYf50sIgYGBAbS0tMRcRDdeSVRGVFXF1KlT8/b6NTU1/EW1gOcrfTxX6eO5sobnK308V9bk+nylqohIbGAlIiKiomIYISIioqIq6zDi8Xiwbt06eDyeYh9KSeD5Sh/PVfp4rqzh+Uofz5U1xTxfJdHASkRERCevsq6MEBERUfExjBAREVFRMYwQERFRUTGMEBERUVGVdRjZsGEDZsyYAa/XiyVLluDFF18s9iEV3a233gpFUWK+Tj/9dPP+0dFRrFy5EpMmTUJVVRU+/elPo6urq4hHXDjPPPMMli1bhpaWFiiKgkceeSTmfiEE1q5di+bmZvh8PrS3t+Ptt9+Oeczx48dx1VVXoaamBnV1dfjiF7+IwcHBAv4tCme88/WFL3xhzO/aRz/60ZjHlMv5Wr9+Pd73vvehuroaDQ0NuPzyy7Fnz56Yx6Tz315HRwcuvfRSVFRUoKGhAV//+tcRCoUK+VfJu3TO1YUXXjjmd+u6666LeUw5nCsA+PGPf4yzzz7bHGTW1taG3//+9+b9dvm9KtswsmnTJqxevRrr1q3Drl27MH/+fCxduhTd3d3FPrSiO/PMM3HkyBHz69lnnzXvu/HGG/G73/0ODz74ILZt24bDhw/jU5/6VBGPtnCGhoYwf/58bNiwIeH9d9xxB77//e9j48aNeOGFF1BZWYmlS5didHTUfMxVV12F1157DU899RQee+wxPPPMM/jSl75UqL9CQY13vgDgox/9aMzv2q9+9auY+8vlfG3btg0rV67E888/j6eeegrBYBAXX3wxhoaGzMeM999eOBzGpZdeikAggOeeew73338/7rvvPqxdu7YYf6W8SedcAcC1114b87t1xx13mPeVy7kCgKlTp+L222/Hzp07sWPHDvzd3/0dPvGJT+C1114DYKPfK1GmFi9eLFauXGn+ORwOi5aWFrF+/foiHlXxrVu3TsyfPz/hfb29vcLlcokHH3zQvO2NN94QAMT27dsLdIT2AEA8/PDD5p81TRNNTU3iP/7jP8zbent7hcfjEb/61a+EEEK8/vrrAoD461//aj7m97//vVAURRw6dKhgx14M8edLCCGuueYa8YlPfCLpc8r5fHV3dwsAYtu2bUKI9P7be+KJJ4SqqqKzs9N8zI9//GNRU1Mj/H5/Yf8CBRR/roQQ4kMf+pC44YYbkj6nXM+VNGHCBPHTn/7UVr9XZVkZCQQC2LlzJ9rb283bVFVFe3s7tm/fXsQjs4e3334bLS0tmDVrFq666ip0dHQAAHbu3IlgMBhz3k4//XRMmzat7M/b/v370dnZGXNuamtrsWTJEvPcbN++HXV1dVi0aJH5mPb2dqiqihdeeKHgx2wHW7duRUNDA0477TRcf/31OHbsmHlfOZ+vvr4+AMDEiRMBpPff3vbt23HWWWehsbHRfMzSpUvR399v/iv4ZBR/rqRf/OIXqK+vx7x587BmzRoMDw+b95XruQqHw3jggQcwNDSEtrY2W/1elcSF8nKtp6cH4XA45uQCQGNjI958880iHZU9LFmyBPfddx9OO+00HDlyBLfddhvOP/98vPrqq+js7ITb7UZdXV3McxobG9HZ2VmcA7YJ+fdP9Dsl7+vs7ERDQ0PM/U6nExMnTizL8/fRj34Un/rUpzBz5ky88847uPnmm3HJJZdg+/btcDgcZXu+NE3DV77yFZx33nmYN28eAKT1315nZ2fC3z9538ko0bkCgCuvvBLTp09HS0sLXn75ZXzjG9/Anj178Jvf/AZA+Z2rV155BW1tbRgdHUVVVRUefvhhzJ07F7t377bN71VZhhFK7pJLLjF/Pvvss7FkyRJMnz4dv/71r+Hz+Yp4ZHSy+Yd/+Afz57POOgtnn302Zs+eja1bt+Kiiy4q4pEV18qVK/Hqq6/G9GpRYsnOVXRf0VlnnYXm5mZcdNFFeOeddzB79uxCH2bRnXbaadi9ezf6+vrw0EMP4ZprrsG2bduKfVgxynKZpr6+Hg6HY0zHcFdXF5qamop0VPZUV1eHU089FXv37kVTUxMCgQB6e3tjHsPzBvPvn+p3qqmpaUyDdCgUwvHjx8v+/AHArFmzUF9fj7179wIoz/O1atUqPPbYY/jTn/6EqVOnmren899eU1NTwt8/ed/JJtm5SmTJkiUAEPO7VU7nyu1245RTTsHChQuxfv16zJ8/H//93/9tq9+rsgwjbrcbCxcuxJYtW8zbNE3Dli1b0NbWVsQjs5/BwUG88847aG5uxsKFC+FyuWLO2549e9DR0VH2523mzJloamqKOTf9/f144YUXzHPT1taG3t5e7Ny503zM008/DU3TzP+zLGfvvfcejh07hubmZgDldb6EEFi1ahUefvhhPP3005g5c2bM/en8t9fW1oZXXnklJsA99dRTqKmpwdy5cwvzFymA8c5VIrt37waAmN+tcjhXyWiaBr/fb6/fq5y1wpaYBx54QHg8HnHfffeJ119/XXzpS18SdXV1MR3D5eirX/2q2Lp1q9i/f7/4y1/+Itrb20V9fb3o7u4WQghx3XXXiWnTpomnn35a7NixQ7S1tYm2trYiH3VhDAwMiJdeekm89NJLAoC46667xEsvvSTeffddIYQQt99+u6irqxOPPvqoePnll8UnPvEJMXPmTDEyMmK+xkc/+lFxzjnniBdeeEE8++yzYs6cOeKKK64o1l8pr1Kdr4GBAfG1r31NbN++Xezfv1/88Y9/FOeee66YM2eOGB0dNV+jXM7X9ddfL2pra8XWrVvFkSNHzK/h4WHzMeP9txcKhcS8efPExRdfLHbv3i02b94sJk+eLNasWVOMv1LejHeu9u7dK7797W+LHTt2iP3794tHH31UzJo1S1xwwQXma5TLuRJCiJtuukls27ZN7N+/X7z88svipptuEoqiiCeffFIIYZ/fq7INI0II8YMf/EBMmzZNuN1usXjxYvH8888X+5CKbvny5aK5uVm43W4xZcoUsXz5crF3717z/pGREfHlL39ZTJgwQVRUVIhPfvKT4siRI0U84sL505/+JACM+brmmmuEEPr23ltuuUU0NjYKj8cjLrroIrFnz56Y1zh27Ji44oorRFVVlaipqRErVqwQAwMDRfjb5F+q8zU8PCwuvvhiMXnyZOFyucT06dPFtddeO+YfA+VyvhKdJwDi5z//ufmYdP7bO3DggLjkkkuEz+cT9fX14qtf/aoIBoMF/tvk13jnqqOjQ1xwwQVi4sSJwuPxiFNOOUV8/etfF319fTGvUw7nSggh/vEf/1FMnz5duN1uMXnyZHHRRReZQUQI+/xeKUIIkbs6CxEREZE1ZdkzQkRERPbBMEJERERFxTBCRERERcUwQkREREXFMEJERERFxTBCRERERcUwQkREREXFMEJERERFxTBCRERERcUwQkREREXFMEJERERFxTBCRERERfX/A6lalO1Kmw2jAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(AJAC_Rfree.T[0])\n",
-    "plt.plot(AJAC_Rfree.T[10])\n",
-    "plt.plot(AJAC_Rfree.T[30])\n",
-    "plt.plot(AJAC_Rfree.T[60])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed5937a4",
-   "metadata": {},
-   "source": [
-    "## Shock to Unemployment Probability"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "943bb3c2",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "CJAC_UnempPrb, AJAC_UnempPrb = Agent.calc_jacobian(\"UnempPrb\", 300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "b423bcb7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(CJAC_UnempPrb.T[0])\n",
-    "plt.plot(CJAC_UnempPrb.T[10])\n",
-    "plt.plot(CJAC_UnempPrb.T[30])\n",
-    "plt.plot(CJAC_UnempPrb.T[60])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "d5399ce7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(AJAC_UnempPrb.T[0])\n",
-    "plt.plot(AJAC_UnempPrb.T[10])\n",
-    "plt.plot(AJAC_UnempPrb.T[30])\n",
-    "plt.plot(AJAC_UnempPrb.T[60])\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,py:percent"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/ConsNewKeynesianModel/Jacobian_Example.ipynb b/examples/ConsNewKeynesianModel/Jacobian_Example.ipynb
new file mode 100644
index 000000000..4ec88e720
--- /dev/null
+++ b/examples/ConsNewKeynesianModel/Jacobian_Example.ipynb
@@ -0,0 +1,327 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4ec2c3a0",
+   "metadata": {},
+   "source": [
+    "# Computing Heterogenous Agent Jacobians in HARK\n",
+    "\n",
+    "By William Du\n",
+    "\n",
+    "This notebook illustrates how to compute Heterogenous Agent Jacobian matrices in HARK.\n",
+    "\n",
+    "These matrices are a fundamental building building block to solving Heterogenous Agent New Keynesian Models with the sequence space jacobian methodology. For more information, see [Auclert, Rognlie, Bardoszy, and Straub (2021)](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA17434)\n",
+    "\n",
+    "For the IndShockConsumerType, Jacobians of Consumption and Saving can be computed with respect to the following parameters:\n",
+    "LivPrb, PermShkStd,TranShkStd,UnempPrb, Rfree, IncUnemp."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37ca5c3e",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from HARK.ConsumptionSaving.ConsNewKeynesianModel import (\n",
+    "    NewKeynesianConsumerType,\n",
+    ")\n",
+    "\n",
+    "import time\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a6459df",
+   "metadata": {},
+   "source": [
+    "## Create Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c49cf2b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dictionary for Agent\n",
+    "Dict = {\n",
+    "    # Solving Parameters\n",
+    "    \"aXtraMax\": 1000,\n",
+    "    \"aXtraCount\": 200,\n",
+    "    # Transition Matrix Simulations Parameters\n",
+    "    \"mMax\": 10000,\n",
+    "    \"mMin\": 1e-6,\n",
+    "    \"mCount\": 300,\n",
+    "    \"mFac\": 3,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aff29a01",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "Agent = NewKeynesianConsumerType(**Dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88715dab",
+   "metadata": {},
+   "source": [
+    "## Compute Steady State"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70f04d67",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "start = time.time()\n",
+    "Agent.compute_steady_state()\n",
+    "print(\"Seconds to compute steady state\", time.time() - start)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e11268d",
+   "metadata": {},
+   "source": [
+    "## Compute Jacobians\n",
+    "\n",
+    "Shocks possible: LivPrb, PermShkStd,TranShkStd, DiscFac,UnempPrb, Rfree, IncUnemp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21b1f860",
+   "metadata": {},
+   "source": [
+    "### Shock to Standard Deviation to Permanent Income Shocks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8ec958b",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "start = time.time()\n",
+    "\n",
+    "CJAC_Perm, AJAC_Perm = Agent.calc_jacobian(\"PermShkStd\", 300)\n",
+    "\n",
+    "print(\"Seconds to calculate Jacobian\", time.time() - start)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc78e4b8",
+   "metadata": {},
+   "source": [
+    "#### Consumption Jacobians"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "979e3529",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(CJAC_Perm.T[0])\n",
+    "plt.plot(CJAC_Perm.T[10])\n",
+    "plt.plot(CJAC_Perm.T[30])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a02ac39",
+   "metadata": {},
+   "source": [
+    "#### Asset Jacobians"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "201fbc06",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(AJAC_Perm.T[0])\n",
+    "plt.plot(AJAC_Perm.T[10])\n",
+    "plt.plot(AJAC_Perm.T[30])\n",
+    "plt.plot(AJAC_Perm.T[60])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ae3a774",
+   "metadata": {},
+   "source": [
+    "## Shock to Real Interest Rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "318efe19",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "CJAC_Rfree, AJAC_Rfree = Agent.calc_jacobian(\"Rfree\", 300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f172f736",
+   "metadata": {},
+   "source": [
+    "#### Consumption Jacobians"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20ae8342",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(CJAC_Rfree.T[0])\n",
+    "plt.plot(CJAC_Rfree.T[10])\n",
+    "plt.plot(CJAC_Rfree.T[30])\n",
+    "plt.plot(CJAC_Rfree.T[60])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22187e1b",
+   "metadata": {},
+   "source": [
+    "#### Asset Jacobians"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "344473d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(AJAC_Rfree.T[0])\n",
+    "plt.plot(AJAC_Rfree.T[10])\n",
+    "plt.plot(AJAC_Rfree.T[30])\n",
+    "plt.plot(AJAC_Rfree.T[60])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed5937a4",
+   "metadata": {},
+   "source": [
+    "## Shock to Unemployment Probability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "943bb3c2",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "CJAC_UnempPrb, AJAC_UnempPrb = Agent.calc_jacobian(\"UnempPrb\", 300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b423bcb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(CJAC_UnempPrb.T[0])\n",
+    "plt.plot(CJAC_UnempPrb.T[10])\n",
+    "plt.plot(CJAC_UnempPrb.T[30])\n",
+    "plt.plot(CJAC_UnempPrb.T[60])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5399ce7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(AJAC_UnempPrb.T[0])\n",
+    "plt.plot(AJAC_UnempPrb.T[10])\n",
+    "plt.plot(AJAC_UnempPrb.T[30])\n",
+    "plt.plot(AJAC_UnempPrb.T[60])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73ee022a-7a01-4922-af76-42ae680dd4b4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,py:percent"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/ConsIndShockModel/IndShockConsumerType_Transition_Matrix_Example.ipynb b/examples/ConsNewKeynesianModel/Transition_Matrix_Example.ipynb
similarity index 96%
rename from examples/ConsIndShockModel/IndShockConsumerType_Transition_Matrix_Example.ipynb
rename to examples/ConsNewKeynesianModel/Transition_Matrix_Example.ipynb
index 44160c834..995183868 100644
--- a/examples/ConsIndShockModel/IndShockConsumerType_Transition_Matrix_Example.ipynb
+++ b/examples/ConsNewKeynesianModel/Transition_Matrix_Example.ipynb
@@ -15,7 +15,7 @@
    "id": "52ce71ba",
    "metadata": {},
    "source": [
-    "This Jupyter Notebook demonstrates how to non-stochastically simulate an economy with transition matrices with functions under the IndShockConsumerType.\n",
+    "This Jupyter Notebook demonstrates how to non-stochastically simulate an economy with transition matrices with functions under the NewKeynesianConsumerType.\n",
     "\n",
     "The three key functions to non stochastically simulate are:\n",
     "\n",
@@ -40,14 +40,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "1f08d05f",
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n",
+    "from HARK.ConsumptionSaving.ConsNewKeynesianModel import NewKeynesianConsumerType\n",
     "\n",
     "\n",
     "import time\n",
@@ -66,21 +66,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "0dc82f9b",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "D:\\Temp\\ipykernel_37140\\3411489450.py:25: RuntimeWarning: divide by zero encountered in log\n",
-      "  \"aNrmInitMean\": np.log(0.0),  # Mean of log initial assets ,\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Dict = {\n",
     "    # Parameters shared with the perfect foresight model\n",
@@ -99,7 +90,6 @@
     "    \"UnempPrbRet\": 0.0000,  # Probability of \"unemployment\" while retired\n",
     "    \"IncUnempRet\": 0.0,  # \"Unemployment\" benefits when retired\n",
     "    \"T_retire\": 0,  # Period of retirement (0 --> no retirement)\n",
-    "    \"tax_rate\": 0.0,  # Flat income tax rate (legacy parameter, will be removed in future)\n",
     "    # A few other parameters\n",
     "    \"BoroCnstArt\": 0.0,  # Artificial borrowing constraint; imposed minimum level of end-of period assets\n",
     "    \"T_cycle\": 1,  # Number of periods in the cycle for this agent type\n",
@@ -138,12 +128,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "fae48368",
    "metadata": {},
    "outputs": [],
    "source": [
-    "example1 = IndShockConsumerType(**Dict)\n",
+    "example1 = NewKeynesianConsumerType(**Dict)\n",
     "example1.cycles = 0\n",
     "example1.solve()"
    ]
@@ -174,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "749d88aa",
    "metadata": {
     "lines_to_next_cell": 2
@@ -208,20 +198,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "74c568e6",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Seconds to calculate both the transition matrix and the steady state distribution 42.12000322341919\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "example1.define_distribution_grid(num_pointsP=110, timestonest=3)\n",
     "p = example1.dist_pGrid  # Grid of permanent income levels\n",
@@ -245,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "cf241bb0",
    "metadata": {
     "lines_to_next_cell": 2
@@ -274,23 +256,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "4508692e",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TranMatrix Assets = [0.41854438]\n",
-      "Simulated Assets = 0.4116282108907816\n",
-      "TranMatrix Consumption = [1.00153889]\n",
-      "Simulated Consumption = 1.0011048645886242\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"TranMatrix Assets = \" + str(AggA))\n",
     "print(\"Simulated Assets = \" + str(Monte_Carlo_Assets))\n",
@@ -657,7 +628,7 @@
    },
    "outputs": [],
    "source": [
-    "ss = IndShockConsumerType(**Dict)\n",
+    "ss = NewKeynesianConsumerType(**Dict)\n",
     "ss.cycles = 0\n",
     "ss.solve()"
    ]
@@ -871,13 +842,16 @@
     "params = deepcopy(Dict)\n",
     "params[\"T_cycle\"] = 20\n",
     "params[\"LivPrb\"] = params[\"T_cycle\"] * [ss.LivPrb[0]]\n",
-    "params[\"PermGroFac\"] = params[\"T_cycle\"] * [1]\n",
+    "params[\"PermGroFac\"] = params[\"T_cycle\"] * [1.0]\n",
     "params[\"PermShkStd\"] = params[\"T_cycle\"] * [ss.PermShkStd[0]]\n",
     "params[\"TranShkStd\"] = params[\"T_cycle\"] * [ss.TranShkStd[0]]\n",
+    "params[\"tax_rate\"] = params[\"T_cycle\"] * [ss.tax_rate[0]]\n",
+    "params[\"labor\"] = params[\"T_cycle\"] * [ss.labor[0]]\n",
+    "params[\"wage\"] = params[\"T_cycle\"] * [ss.wage[0]]\n",
     "params[\"Rfree\"] = params[\"T_cycle\"] * [ss.Rfree]\n",
     "params[\"DiscFac\"] = params[\"T_cycle\"] * [ss.DiscFac]\n",
     "\n",
-    "FinHorizonAgent = IndShockConsumerType(**params)\n",
+    "FinHorizonAgent = NewKeynesianConsumerType(**params)\n",
     "FinHorizonAgent.cycles = 1\n",
     "\n",
     "FinHorizonAgent.del_from_time_inv(\n",
diff --git a/examples/SSJ-Example/KS-HARK-presentation.ipynb b/examples/SSJ-Example/KS-HARK-presentation.ipynb
new file mode 100644
index 000000000..c4a3ec361
--- /dev/null
+++ b/examples/SSJ-Example/KS-HARK-presentation.ipynb
@@ -0,0 +1,585 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "602e0365",
+   "metadata": {},
+   "source": [
+    "# Solving Krusell Smith Model with HARK and SSJ\n",
+    "\n",
+    "By William Du"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46e98a1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install sequence_jacobian -q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58617d0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from scipy import optimize\n",
+    "from sequence_jacobian import create_model, simple  # functions\n",
+    "from sequence_jacobian.classes import JacobianDict, SteadyStateDict\n",
+    "\n",
+    "from HARK.ConsumptionSaving.ConsNewKeynesianModel import NewKeynesianConsumerType"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50a586f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calibration = {\n",
+    "    \"eis\": 1,\n",
+    "    \"delta\": 0.025,\n",
+    "    \"alpha\": 0.11,\n",
+    "    \"L\": 1.0,\n",
+    "    \"K\": 1.0,\n",
+    "    \"Y\": 1.0,\n",
+    "    \"r\": 0.01,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a97701f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "L_ss = 1.0  # Steady state labor\n",
+    "r_ss = 0.01  # steady state interest rate\n",
+    "Y_ss = 1.0  # steady state output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bac99bf",
+   "metadata": {},
+   "source": [
+    "## \n",
+    "\n",
+    "Given these steady state choices, we will need to find $K, Z$ to clear the firm first order conditions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e8a59d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.optimize import root\n",
+    "\n",
+    "\n",
+    "def your_funcs(X):\n",
+    "    L = calibration[\"L\"]\n",
+    "    alpha = calibration[\"alpha\"]\n",
+    "    delta = calibration[\"delta\"]\n",
+    "\n",
+    "    K, Z = X\n",
+    "    # all RHS have to be 0\n",
+    "    f = [\n",
+    "        alpha * Z * (K / L) ** (alpha - 1) - delta - r_ss,  # r = MPK\n",
+    "        Z * K**alpha * L ** (1 - alpha) - Y_ss,  # Y = Z*F(K,L)\n",
+    "    ]\n",
+    "\n",
+    "    return f\n",
+    "\n",
+    "\n",
+    "sol = root(your_funcs, [1.0, 1.0])  # find roots\n",
+    "\n",
+    "K_ss, Z_ss = sol.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e580acc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(sol)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "922dce5d",
+   "metadata": {},
+   "source": [
+    " Let's double check the roots we find produce our chosen steady state values for $ r, Y , L$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b15804e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def firm(\n",
+    "    K, Z, L=calibration[\"L\"], alpha=calibration[\"alpha\"], delta=calibration[\"delta\"]\n",
+    "):\n",
+    "    r = alpha * Z * (K / L) ** (alpha - 1) - delta\n",
+    "    w = (1 - alpha) * Z * (K / L) ** alpha\n",
+    "    Y = Z * K**alpha * L ** (1 - alpha)\n",
+    "    return r, w, Y\n",
+    "\n",
+    "\n",
+    "r_ss, w_ss, Y_ss = firm(sol.x[0], sol.x[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08aa3eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(r_ss, w_ss, Y_ss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a3c40a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "HANK_Dict = {\n",
+    "    # Parameters shared with the perfect foresight model\n",
+    "    \"CRRA\": calibration[\"eis\"],  # Coefficient of relative risk aversion\n",
+    "    \"Rfree\": (1 + r_ss),  # Interest factor on assets\n",
+    "    \"DiscFac\": 0.98,  # Intertemporal discount factor\n",
+    "    \"LivPrb\": [0.99375],  # Survival probability\n",
+    "    \"PermGroFac\": [1.00],  # Permanent income growth factor\n",
+    "    # Parameters that specify the income distribution over the lifecycle\n",
+    "    # Standard deviation of log permanent shocks to income\n",
+    "    \"PermShkStd\": [0.06],\n",
+    "    \"PermShkCount\": 5,  # Number of points in discrete approximation to permanent income shocks\n",
+    "    # Standard deviation of log transitory shocks to income\n",
+    "    \"TranShkStd\": [0.2],\n",
+    "    \"TranShkCount\": 5,\n",
+    "    # HANK params\n",
+    "    \"tax_rate\": [\n",
+    "        0\n",
+    "    ],  # set to 0.0 because we are going to assume that labor here is actually after tax income\n",
+    "    \"labor\": [L_ss],\n",
+    "    \"wage\": [w_ss],\n",
+    "    # Number of points in discrete approximation to transitory income shocks\n",
+    "    \"UnempPrb\": 0.0,  # Probability of unemployment while working\n",
+    "    \"IncUnemp\": 0.0,  # Unemployment benefits replacement rate\n",
+    "    \"UnempPrbRet\": 0.0000,  # Probability of \"unemployment\" while retired\n",
+    "    \"IncUnempRet\": 0.0,  # \"Unemployment\" benefits when retired\n",
+    "    \"T_retire\": 0.0,  # Period of retirement (0 --> no retirement)\n",
+    "    # Parameters for constructing the \"assets above minimum\" grid\n",
+    "    \"aXtraMin\": 0.0001,  # Minimum end-of-period \"assets above minimum\" value\n",
+    "    \"aXtraMax\": 2000,  # Maximum end-of-period \"assets above minimum\" value\n",
+    "    \"aXtraCount\": 200,  # Number of points in the base grid of \"assets above minimum\"\n",
+    "    # Exponential nesting factor when constructing \"assets above minimum\" grid\n",
+    "    \"aXtraNestFac\": 3,\n",
+    "    \"aXtraExtra\": None,  # Additional values to add to aXtraGrid\n",
+    "    # Transition Matrix simulation parameters\n",
+    "    \"mCount\": 200,\n",
+    "    \"mMax\": 2000,\n",
+    "    \"mMin\": 0.0001,\n",
+    "    \"mFac\": 3,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3be9593e",
+   "metadata": {},
+   "source": [
+    "# Create HARK agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2754eb05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Agent = NewKeynesianConsumerType(**HANK_Dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa266888",
+   "metadata": {},
+   "source": [
+    "# Find Steady state discount factor clear asset market\n",
+    "\n",
+    "We will estimate the discount factor to ensure that asset supply equals the steady state capital we found earlier. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b97b7a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def A_ss_func(beta):\n",
+    "    HANK_Dict[\"DiscFac\"] = beta\n",
+    "\n",
+    "    Agent_func = NewKeynesianConsumerType(**HANK_Dict, verbose=False)\n",
+    "    A_ss = Agent_func.compute_steady_state()[0]\n",
+    "\n",
+    "    return A_ss\n",
+    "\n",
+    "\n",
+    "def ss_dif(beta):\n",
+    "    return A_ss_func(beta) - Asset_target\n",
+    "\n",
+    "\n",
+    "start = time.time()\n",
+    "Asset_target = K_ss\n",
+    "\n",
+    "DiscFac = optimize.brentq(ss_dif, 0.8, 0.9999)\n",
+    "\n",
+    "print(\"Time taken to solve for steady state\", time.time() - start)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2dfab8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a new agent\n",
+    "HANK_Dict[\"DiscFac\"] = DiscFac\n",
+    "Agent_GE = NewKeynesianConsumerType(**HANK_Dict, verbose=False)\n",
+    "A_ss, C_ss = Agent_GE.compute_steady_state()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f59e006",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to make sure goods and asset markets clear\n",
+    "print(\"goods_clearing\", Y_ss - C_ss - calibration[\"delta\"] * K_ss)\n",
+    "print(\"asset_clearing\", A_ss - K_ss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8167383",
+   "metadata": {},
+   "source": [
+    "# Computing Heterogenous Agent Jacobians"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c05c0391",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start = time.time()\n",
+    "\n",
+    "CJACW, AJACW = Agent_GE.calc_jacobian(\"wage\", 300)  # Wage jacobians\n",
+    "\n",
+    "print(\"Time taken to compute jacobians\", time.time() - start)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39f4d57e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(CJACW.T[0])\n",
+    "plt.plot(CJACW.T[20])\n",
+    "plt.plot(CJACW.T[50])\n",
+    "plt.plot(CJACW.T[100])\n",
+    "plt.xlim(-2, 300)\n",
+    "plt.plot(np.arange(300), np.zeros(300), color=\"k\")\n",
+    "plt.title(\"Consumption Jacobian Wage\")\n",
+    "plt.xlabel(\"quarters\")\n",
+    "plt.ylabel(\"C response\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e072e75a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start = time.time()\n",
+    "\n",
+    "CJACR, AJACR = Agent_GE.calc_jacobian(\"Rfree\", 300)  # Rfree jacobians\n",
+    "\n",
+    "print(\"Time taken to compute jacobians\", time.time() - start)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72b5f7b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(CJACR.T[0])\n",
+    "plt.plot(CJACR.T[30])\n",
+    "plt.plot(CJACR.T[50])\n",
+    "plt.plot(np.arange(300), np.zeros(300), color=\"k\")\n",
+    "plt.title(\"Consumption Jacobian interest rate\")\n",
+    "plt.xlabel(\"quarters\")\n",
+    "plt.ylabel(\"C response\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03d2d6a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Store Jacobians in JacobianDict Object\n",
+    "Jacobian_Dict = JacobianDict(\n",
+    "    {\n",
+    "        \"C\": {\n",
+    "            \"w\": CJACW,\n",
+    "            \"r\": CJACR,\n",
+    "        },\n",
+    "        \"A\": {\n",
+    "            \"w\": AJACW,\n",
+    "            \"r\": AJACR,\n",
+    "        },\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Construct SteadyStateDict object\n",
+    "SteadyState_Dict = SteadyStateDict(\n",
+    "    {\n",
+    "        \"asset_mkt\": 0.0,\n",
+    "        \"goods_mkt\": 0.0,\n",
+    "        \"r\": r_ss,\n",
+    "        \"Y\": Y_ss,\n",
+    "        \"A\": K_ss,\n",
+    "        \"C\": C_ss,\n",
+    "        \"Z\": Z_ss,\n",
+    "        \"delta\": calibration[\"delta\"],\n",
+    "        \"alpha\": calibration[\"alpha\"],\n",
+    "        \"L\": L_ss,\n",
+    "        \"K\": K_ss,\n",
+    "        \"w\": w_ss,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3669fab",
+   "metadata": {},
+   "source": [
+    "## Other Blocks of the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87abc1e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@simple\n",
+    "def firm(K, L, Z, alpha, delta):\n",
+    "    r = alpha * Z * (K(-1) / L) ** (alpha - 1) - delta\n",
+    "    w = (1 - alpha) * Z * (K(-1) / L) ** alpha\n",
+    "    Y = Z * K(-1) ** alpha * L ** (1 - alpha)\n",
+    "    return r, w, Y\n",
+    "\n",
+    "\n",
+    "@simple\n",
+    "def mkt_clearing(K, A, Y, C, delta):\n",
+    "    asset_mkt = A - K\n",
+    "    goods_mkt = Y - C - delta * K\n",
+    "    return asset_mkt, goods_mkt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f95cd93a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ks = create_model([Jacobian_Dict, firm, mkt_clearing], name=\"Krusell-Smith\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3d569b5",
+   "metadata": {},
+   "source": [
+    "# Solving for Impulse Responses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48330acf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T = 300  # <-- the length of the IRF\n",
+    "rho_Z = 0.8  # persistence of IRF shock\n",
+    "dZ = 0.001 * Z_ss * rho_Z ** np.arange(T)\n",
+    "shocks = {\"Z\": dZ}\n",
+    "\n",
+    "inputs = [\"Z\"]\n",
+    "unknowns = [\"K\"]\n",
+    "targets = [\"asset_mkt\"]\n",
+    "\n",
+    "\n",
+    "irfs_Z = ks.solve_impulse_linear(SteadyState_Dict, unknowns, targets, shocks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f5a085f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_irfs(\n",
+    "    irfs_list,\n",
+    "    variables,\n",
+    "    labels=[\" \"],\n",
+    "    ylabel=r\"Percentage points (dev. from ss)\",\n",
+    "    T_plot=50,\n",
+    "    figsize=(18, 6),\n",
+    "):\n",
+    "    if len(irfs_list) != len(labels):\n",
+    "        labels = [\" \"] * len(irfs_list)\n",
+    "    n_var = len(variables)\n",
+    "    fig, ax = plt.subplots(1, n_var, figsize=figsize, sharex=True)\n",
+    "    for i in range(n_var):\n",
+    "        # plot all irfs\n",
+    "        for j, irf in enumerate(irfs_list):\n",
+    "            ax[i].plot(irf[variables[i]][:50], label=labels[j])\n",
+    "        ax[i].set_title(variables[i])\n",
+    "        ax[i].set_xlabel(r\"$t$\")\n",
+    "        if i == 0:\n",
+    "            ax[i].set_ylabel(ylabel)\n",
+    "        ax[i].legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71cceab3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Impulse Responses to Productivity Shock\n",
+    "show_irfs([irfs_Z], [\"Y\", \"C\", \"Z\", \"K\", \"r\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0130fc3",
+   "metadata": {},
+   "source": [
+    "# Simulating the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9887a17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from estimation.plots import plot_timeseries\n",
+    "from estimation.routines import simulate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74dbf468",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outputs = [\"Y\", \"K\", \"r\"]\n",
+    "\n",
+    "sigmas = {\"Z\": 0.001}\n",
+    "rhos = {\"Z\": 0.8}\n",
+    "impulses = {}\n",
+    "\n",
+    "\n",
+    "for i in inputs:\n",
+    "    own_shock = {i: sigmas[i] * rhos[i] ** np.arange(T)}\n",
+    "    impulses[i] = ks.solve_impulse_linear(\n",
+    "        SteadyState_Dict, unknowns, targets, own_shock\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "T_sim = 156  # 39 years, as in the original SW (2007) sample\n",
+    "data_simul = simulate(list(impulses.values()), outputs, T_sim)\n",
+    "plot_timeseries(data_simul, (1, 3), figsize=(12, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27732edd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/examples/SSJ-Example/SSJ_example.ipynb b/examples/SSJ-Example/SSJ_example.ipynb
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+++ b/examples/SSJ-Example/SSJ_example.ipynb
@@ -0,0 +1,527 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9aff8c24",
+   "metadata": {},
+   "source": [
+    "# HARK and SSJ example\n",
+    "\n",
+    "By William Du (wdu9@jhu.edu) \n",
+    "\n",
+    "\n",
+    "- This notebook demonstrates how HARK and the Sequence Space Jacobian toolkit can be combined to solve HANK models.\n",
+    "\n",
+    "- HARK was designed to solve rich microeconomic models:\n",
+    "    - Lifecycle models\n",
+    "    - Discrete continuous choice\n",
+    "    - Portfolio choice. \n",
+    "    \n",
+    "<br />\n",
+    "\n",
+    "- SSJ was created to solve HANK models with incredibly speed and ease\n",
+    "\n",
+    "- Connecting HARK to SSJ allows us to solve macro models with richer micro features.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf4b8cce",
+   "metadata": {},
+   "source": [
+    "# Model\n",
+    "\n",
+    "\n",
+    "To begin a basic HANK model is introduced. The model follows these lecture [SSJ slides](https://github.com/shade-econ/nber-workshop-2022/blob/main/Lectures/Lecture%204%20Fiscal%20Policy.pdf) from their heterogeneous agent workshop.\n",
+    "\n",
+    "\n",
+    "- Heterogenous agent households that consume/save into one asset. \n",
+    "\n",
+    "- Households face idiosyncratic income shocks.\n",
+    "\n",
+    "- Cobb Douglas Production function with Labor.\n",
+    "\n",
+    "- Sticky Wages\n",
+    "\n",
+    "- Government taxes labor income, rolls over debt, to pay its expenditures.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10546d3",
+   "metadata": {},
+   "source": [
+    "# Bringing Model to Code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49d75e2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from HARK.ConsumptionSaving.ConsNewKeynesianModel import (\n",
+    "    NewKeynesianConsumerType,\n",
+    ")\n",
+    "\n",
+    "from sequence_jacobian.classes import JacobianDict, SteadyStateDict\n",
+    "import sequence_jacobian as sj  # SSJ will allow us to define blocks, models, compute IRFs, etc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df3f455b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np  # '1.22.0'\n",
+    "import time\n",
+    "from scipy import optimize  # scipy version '1.10.0'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b676fc68",
+   "metadata": {},
+   "source": [
+    "### Calibrate Steady State Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fa9a283",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Steady state parameters\n",
+    "r_ss = 0.03  # Interest rate\n",
+    "G_ss = 0.2  # government spending\n",
+    "B_ss = 0.2  # government debt\n",
+    "Y_ss = 1.0  # output\n",
+    "\n",
+    "T_ss = G_ss + r_ss * B_ss  # Lump sum taxes\n",
+    "Z_ss = Y_ss - T_ss  # after tax labor income\n",
+    "deficit_ss = G_ss - T_ss  # deficit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4a84511",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "HANK_dict = {\n",
+    "    \"cycles\": 0,  # Infinite horizon problem\n",
+    "    # Parameters shared with the perfect foresight model\n",
+    "    \"Rfree\": 1.0 + r_ss,  # Interest factor on assets\n",
+    "    \"LivPrb\": [0.99375],  # Survival probability\n",
+    "    # Parameters that specify the income distribution over the lifecycle\n",
+    "    \"PermShkStd\": [0.06],  # Standard deviation of log permanent shocks to income\n",
+    "    \"PermShkCount\": 5,  # Number of points in discrete approximation to permanent income shocks\n",
+    "    \"TranShkStd\": [0.2],  # Standard deviation of log transitory shocks to income\n",
+    "    \"TranShkCount\": 5,  # Number of points in discrete approximation to transitory income shocks\n",
+    "    # HANK parameters\n",
+    "    \"tax_rate\": [\n",
+    "        0.0\n",
+    "    ],  # set to 0.0 because we are going to assume that labor here is actually after tax income\n",
+    "    \"labor\": [Z_ss],\n",
+    "    \"wage\": [1.0],\n",
+    "    \"UnempPrb\": 0.0,  # Probability of unemployment while working\n",
+    "    \"IncUnemp\": 0.0,  # Unemployment benefits replacement rate\n",
+    "    # Parameters for constructing the \"assets above minimum\" grid\n",
+    "    \"aXtraMax\": 50,  # Maximum end-of-period \"assets above minimum\" value\n",
+    "    \"aXtraCount\": 100,  # Number of points in the base grid of \"assets above minimum\"\n",
+    "    \"BoroCnstArt\": 0.0,\n",
+    "    # Transition matrix simulation parameters\n",
+    "    \"mCount\": 200,\n",
+    "    \"mMax\": 50,\n",
+    "    \"mMin\": 1e-5,\n",
+    "    \"mFac\": 3,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b97096a",
+   "metadata": {},
+   "source": [
+    "# Solve for Steady State"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac7ec69c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ss_func(beta):\n",
+    "    HANK_dict[\"DiscFac\"] = beta\n",
+    "    TempAgent = NewKeynesianConsumerType(**HANK_dict, verbose=False)\n",
+    "    A_ss = TempAgent.compute_steady_state()[0]\n",
+    "    return A_ss\n",
+    "\n",
+    "\n",
+    "def ss_dif(beta):\n",
+    "    return ss_func(beta) - Asset_target\n",
+    "\n",
+    "\n",
+    "Asset_target = B_ss\n",
+    "DiscFac = optimize.brentq(ss_dif, 0.85, 0.99)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc28006a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a HARK agent\n",
+    "HANK_dict[\"DiscFac\"] = DiscFac\n",
+    "Agent_GE = NewKeynesianConsumerType(**HANK_dict, verbose=False)\n",
+    "A_ss, C_ss = Agent_GE.compute_steady_state()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4fadaab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"goods_clearing\", Y_ss - C_ss - G_ss)\n",
+    "print(\"asset_clearing\", A_ss - B_ss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7336168",
+   "metadata": {},
+   "source": [
+    "## Compute Jacobians"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea09fc75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start = time.time()\n",
+    "\n",
+    "CJACR, AJACR = Agent_GE.calc_jacobian(\"Rfree\", 300)\n",
+    "CJACZ, AJACZ = Agent_GE.calc_jacobian(\"labor\", 300)\n",
+    "\n",
+    "print(\"Time taken to compute jacobians\", time.time() - start)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce169ef9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(CJACR.T[0])\n",
+    "plt.plot(CJACR.T[30])\n",
+    "plt.plot(CJACR.T[50])\n",
+    "plt.xlim(-2, 100)\n",
+    "plt.title(\"Consumption Jacobians wrt Interest rate\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6eff21bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(CJACZ.T[0])\n",
+    "plt.plot(CJACZ.T[20])\n",
+    "plt.plot(CJACZ.T[50])\n",
+    "plt.xlim(-2, 100)\n",
+    "plt.title(\"Consumption Jacobians wrt Income\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe6feb98",
+   "metadata": {},
+   "source": [
+    "# Specify Government Budget Constraint and Market Clearing Conditions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b35ed9e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Government Budget Constraint\n",
+    "@sj.simple\n",
+    "def fiscal(B, r, G, Y):\n",
+    "    T = (1 + r) * B(-1) + G - B  # total tax burden\n",
+    "    Z = Y - T  # after tax income\n",
+    "    deficit = G - T\n",
+    "    return T, Z, deficit\n",
+    "\n",
+    "\n",
+    "# Market Clearing Conditions\n",
+    "@sj.simple\n",
+    "def mkt_clearing(A, B, Y, C, G):\n",
+    "    asset_mkt = A - B\n",
+    "    goods_mkt = Y - C - G\n",
+    "    return asset_mkt, goods_mkt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5710651",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Store Jacobians in JacobianDict Object\n",
+    "Jacobian_dict = JacobianDict({\"C\": {\"Z\": CJACZ}, \"A\": {\"Z\": AJACZ}})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffe65da0-7cea-4d60-9afe-f89adae896f8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Construct SteadyStateDict object\n",
+    "SteadyState_dict = SteadyStateDict(\n",
+    "    {\n",
+    "        \"asset_mkt\": 0.0,\n",
+    "        \"goods_mkt\": 0.0,\n",
+    "        \"deficit\": G_ss - T_ss,\n",
+    "        \"B\": B_ss,  # Gov. Debt\n",
+    "        \"r\": r_ss,  # Interest rate\n",
+    "        \"G\": G_ss,  # Gov. Spending\n",
+    "        \"Y\": Y_ss,  # Output\n",
+    "        \"A\": B_ss,  # Assets\n",
+    "        \"C\": C_ss,  # Consumption\n",
+    "        \"Z\": Y_ss - T_ss,  # Income (post-tax)\n",
+    "        \"T\": T_ss,  # Lump Sum Taxes\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbce1742",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T = 300  # <-- the length of the IRF\n",
+    "rho_G = 0.8  # peristence of shock\n",
+    "dG = 0.01 * rho_G ** np.arange(T)\n",
+    "shocks = {\"G\": dG}\n",
+    "\n",
+    "unknowns_td = [\"Y\"]\n",
+    "targets_td = [\"asset_mkt\"]\n",
+    "\n",
+    "# Create model\n",
+    "ha_lin = sj.create_model([Jacobian_dict, fiscal, mkt_clearing], name=\"HA Model\")\n",
+    "\n",
+    "# obtain impulse responses\n",
+    "irfs_G_lin = ha_lin.solve_impulse_linear(\n",
+    "    SteadyState_dict, unknowns_td, targets_td, shocks\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93a0c5fc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def show_irfs(\n",
+    "    irfs_list,\n",
+    "    variables,\n",
+    "    labels=[\" \"],\n",
+    "    ylabel=r\"Percentage points (dev. from ss)\",\n",
+    "    T_plot=50,\n",
+    "    figsize=(18, 6),\n",
+    "):\n",
+    "    if len(irfs_list) != len(labels):\n",
+    "        labels = [\" \"] * len(irfs_list)\n",
+    "    n_var = len(variables)\n",
+    "    fig, ax = plt.subplots(1, n_var, figsize=figsize, sharex=True)\n",
+    "    for i in range(n_var):\n",
+    "        # plot all irfs\n",
+    "        for j, irf in enumerate(irfs_list):\n",
+    "            ax[i].plot(irf[variables[i]][:50], label=labels[j])\n",
+    "        ax[i].set_title(variables[i])\n",
+    "        ax[i].set_xlabel(r\"$t$\")\n",
+    "        if i == 0:\n",
+    "            ax[i].set_ylabel(ylabel)\n",
+    "        ax[i].legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "523314a5",
+   "metadata": {},
+   "source": [
+    "## Government Spending Shock (Balanced Budget)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d287b37c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "show_irfs([irfs_G_lin], [\"G\", \"Y\", \"T\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6b166b2",
+   "metadata": {},
+   "source": [
+    "## Government Spending shock (deficit financed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "603c9385",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rho_B = 0.9\n",
+    "dB = np.cumsum(dG) * rho_B ** np.arange(T)\n",
+    "shocks_B = {\"G\": dG, \"B\": dB}\n",
+    "\n",
+    "irfs_B_lin = ha_lin.solve_impulse_linear(\n",
+    "    SteadyState_dict, unknowns_td, targets_td, shocks_B\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "332b34b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "show_irfs(\n",
+    "    [irfs_G_lin, irfs_B_lin],\n",
+    "    [\"G\", \"Y\", \"T\", \"deficit\", \"goods_mkt\"],\n",
+    "    labels=[\"balanced budget\", \"deficit financed\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5030fdc",
+   "metadata": {},
+   "source": [
+    "## Nonlinear Impulse Responses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f7d1534",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NOTE: These cells have been commented out because they rely on files that are not present in the HARK repo.\n",
+    "\n",
+    "# def hh(Z):\n",
+    "#    C,A = Agent_GE.calc_agg_path(Z,300)\n",
+    "#    return C,A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29783be3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from Misc_Utilities.simple_block import exog # import wrapper so SSJ can identify HARK household object\n",
+    "\n",
+    "# hh_block = exog(Jacobian_dict,hh,hh)\n",
+    "# hh_block.name = hh\n",
+    "\n",
+    "# ha = sj.create_model([hh_block,fiscal,mkt_clearing], name=\"HA Model\")\n",
+    "# irfs_G_nonlin = ha.solve_impulse_nonlinear(SteadyState_dict, unknowns_td, targets_td, shocks)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a327b50f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plt.plot(irfs_G_nonlin['Y'], label ='nonlinear')\n",
+    "# plt.plot(irfs_G_lin['Y'], label = 'linear', linestyle='--')\n",
+    "# plt.xlim(-1,50)\n",
+    "# plt.legend()\n",
+    "# plt.title('Nonlinear Impulse responses of output')\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "558d31cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show_irfs([irfs_G_lin,irfs_G_nonlin], ['G', 'Y','T'], ['Linear', 'Nonlinear'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/SSJ-Example/SSJ_explanation.ipynb b/examples/SSJ-Example/SSJ_explanation.ipynb
new file mode 100644
index 000000000..369bfffe8
--- /dev/null
+++ b/examples/SSJ-Example/SSJ_explanation.ipynb
@@ -0,0 +1,363 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5d86d99a",
+   "metadata": {},
+   "source": [
+    "# Sequence Space Jacobian method to Solve HANK models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d54e4a66",
+   "metadata": {},
+   "source": [
+    "- The sequence space jacobian method is a computational method to solve general equilibrium models (with both rich micro heterogeneity and without)\n",
+    "\n",
+    "\n",
+    "\n",
+    "- Method linearizes the model to first order to solve for solutions to small MIT shocks.\n",
+    "\n",
+    "\n",
+    "- The algorithm assumes that agents have perfect foresight on macro aggregates (i.e. Model is deterministic with respect to aggregate states).\n",
+    "\n",
+    "\n",
+    "- Method allows for the fast computation of jacobian matrices that summarize the aggregate behavior of agents with rich micro heterogeneity (Heterogenous households, firms, etc.)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10f78487",
+   "metadata": {},
+   "source": [
+    "# Advantages of SSJ\n",
+    "\n",
+    "Can solve general equilibrium models with rich microeconomic heterogeneity quickly. (Basic HANK models can take 3 seconds, previous methods take at least 15 minutes)\n",
+    "\n",
+    "Can add additional exogenous shocks at virtually no cost.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "095bae35",
+   "metadata": {},
+   "source": [
+    "## Krusell Smith Model in Sequence Space"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f1bf1b5",
+   "metadata": {},
+   "source": [
+    " ### Households\n",
+    "\n",
+    "Assume a continuum of atomistic households on the unit interval $[0,1]$ indexed by i. \n",
+    "\n",
+    "Assume households have perfect foresight over the real interest rate $r_{t}$ and the real wage $w_{t}$.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e0864c0",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "$$\n",
+    "V_{t}(m_{t}) = \\max_{c_{t}} u(c_{t}) + E_{t}\\left[ V_{t+1}(m_{t+1}) \\right]\n",
+    "$$\n",
+    "\n",
+    "#### Budget Constraint\n",
+    "\n",
+    "$$ m_{ t} = z_{t}  + (1+\\mathit{r}_{t})k_{ t-1}$$\n",
+    "\n",
+    "$$ c_{ t}  + k_{t}= z_{ t}  + (1+\\mathit{r}_{t}) k_{ t-1}  $$ \n",
+    "\n",
+    "$$k_{t} \\geq 0$$\n",
+    "\n",
+    "where \n",
+    "\n",
+    "$m_{t}$ is cash on hand.\n",
+    "\n",
+    "$k_{t}$ value capital holdings.\n",
+    "\n",
+    "$z_{t}$ is labor income.\n",
+    "\n",
+    "\n",
+    "#### Labor Income\n",
+    "\n",
+    "Labor income is composed of permanent component $\\mathit{p}$ and transitory component $\\theta$. \n",
+    "\n",
+    "\n",
+    "$$z_{t} =  \\theta_{it} w_{t} \\bar{L}$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "$ log(\\theta_{it}) \\sim N\\left(-\\frac{\\sigma_{\\theta}^2}{2}, \\sigma_{\\theta}^2 \\right)$ \n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "405ddb01",
+   "metadata": {},
+   "source": [
+    "## Firms\n",
+    "\n",
+    "$$Y_{t} = Z_{t}K_{t}^{\\alpha}L_{t}^{1-\\alpha}$$\n",
+    "\n",
+    "### F.O.C.\n",
+    "\n",
+    "$$\n",
+    "w_{t} = (1-\\alpha) Z_{t} K_{t}^{\\alpha}L_{t}^{-\\alpha}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "r_{t} = \\alpha  Z_{t} K_{t}^{\\alpha - 1} L_{t}^{1-\\alpha}\n",
+    "$$\n",
+    "\n",
+    "where $log(Z_{t}) = \\rho_{Z} log( Z_{t-1}) + \\epsilon_{Z}$\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebeea820",
+   "metadata": {},
+   "source": [
+    "## Market Clearing \n",
+    "\n",
+    "\n",
+    "$$ K_{t} = \\mathcal{K}_{t}\\left(\\left\\{r_{s},w_{s}\\right\\}_{s=0}^{T} \\right)$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "$$L_{t} = \\bar{L}$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "where $$\\mathcal{K}_{t}\\left(\\left\\{r_{s},w_{s}\\right\\}_{s=0}^{T} \\right) = \\int_{0}^{1} k_{it}(\\left\\{r_{s},w_{s}\\right\\}_{s=0}^{T}) di = \\int k_{t}(m, \\left\\{r_{s},w_{s}\\right\\}_{s=0}^{T}) d\\Phi(m)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12cc8957",
+   "metadata": {},
+   "source": [
+    "# Define model as a system of difference equations in sequence space"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01ee68b8",
+   "metadata": {},
+   "source": [
+    "$$ H_{t}(\\mathbf{U},\\mathbf{Z}) = \\begin{pmatrix} \n",
+    " \\mathcal{K}_{t}(\\{r_{s} ,w_{s} \\}_{s=0}^{s=T}) - K_{t}  \\\\ \\\\\n",
+    "  w_{t} - (1-\\alpha) Z_{t} K_{t}^{\\alpha} \\bar{L}^{-\\alpha} \\\\ \\\\\n",
+    "r_{t} - \\alpha  Z_{t} K_{t}^{\\alpha - 1} \\bar{L}^{1-\\alpha} \\\\ \\\\\n",
+    " \\end{pmatrix} = \\begin{pmatrix} 0 \\\\ 0  \\\\ 0\\\\ \\end{pmatrix} , \\quad t=0,1 ,2,3,....$$\n",
+    " \n",
+    " where \n",
+    " \n",
+    "\n",
+    " \n",
+    " $\\mathbf{U} = \\left(K_{t} , r_{t} , w_{t} \\right)_{t=0}^{t=T}$ \n",
+    " \n",
+    " $\\mathbf{Z} = \\left(Z_{t}\\right)_{t=0}^{t=T}$\n",
+    " \n",
+    "Now define $$\\mathbf{H}(\\mathbf{U},\\mathbf{Z})= \\begin{pmatrix} \n",
+    "H_{0}(\\mathbf{U},\\mathbf{Z}) \\\\ \\\\\n",
+    ". \\\\ \\\\\n",
+    ". \\\\ \\\\\n",
+    " H_{t}(\\mathbf{U},\\mathbf{Z}) \\\\ \\\\\n",
+    " . \\\\ \\\\\n",
+    " . \\\\ \\\\\n",
+    "H_{T}(\\mathbf{U},\\mathbf{Z}) \\\\ \\\\\n",
+    " \\end{pmatrix} = \\begin{pmatrix} 0 \\\\ .\\\\ 0 \\\\ . \\\\ 0\\\\ \\end{pmatrix}$$\n",
+    " \n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a545e7ca",
+   "metadata": {},
+   "source": [
+    "# How to solve this model?\n",
+    "\n",
+    "- Linearize around steady state.\n",
+    "\n",
+    "By implicit function theorem\n",
+    "\n",
+    "\n",
+    "$$ d\\mathbf{U} = -\\mathbf{H}_{\\mathbf{U}}^{-1}(\\mathbf{U}_{ss},\\mathbf{Z}_{ss}) \\mathbf{H}_{\\mathbf{Z}}(\\mathbf{U}_{ss},\\mathbf{Z}_{ss}) d\\mathbf{Z}$$\n",
+    "\n",
+    "or to be trite\n",
+    "\n",
+    "$$ d\\mathbf{U} = -\\mathbf{H}_{\\mathbf{U}}^{-1} \\mathbf{H}_{\\mathbf{Z}} d\\mathbf{Z}$$\n",
+    "\n",
+    "\n",
+    "where $d\\mathbf{U} = \\left(dK_{t} , dr_{t} , dw_{t} \\right)_{t=0}^{t=T}$ is the endogenous impulse response to some exogenous shock $d\\mathbf{Z} = \\left(dZ_{t}\\right)_{t=0}^{t=T}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "237520d5",
+   "metadata": {},
+   "source": [
+    "## Heterogenous Agent Jacobian \n",
+    "\n",
+    "When computing $-\\mathbf{H}_{\\mathbf{U}}^{-1}(\\mathbf{U}_{ss},\\mathbf{Z}_{ss})$ we will need to compute the jacobian of  $$\\mathbf{F}(\\mathbf{r},\\mathbf{w}) =  \\begin{pmatrix} \\mathcal{K}_{0}(\\{r_{s} ,w_{s} \\}_{s=0}^{s=T})\\\\ \\\\\n",
+    ".\\\\ \\\\\n",
+    ". \\\\ \\\\\n",
+    "\\mathcal{K}_{t}(\\{r_{s} ,w_{s} \\}_{s=0}^{s=T})\\\\ \\\\\n",
+    ".\\\\ \\\\\n",
+    ".\\\\ \\\\\n",
+    "\\mathcal{K}_{T}(\\{r_{s} ,w_{s} \\}_{s=0}^{s=T})\\\\ \\\\\n",
+    "\\end{pmatrix} $$ \n",
+    "\n",
+    "\n",
+    "More specifically we will need the Jacobian Matrix $\\mathbf{F}_{\\mathbf{r}}(\\mathbf{r},\\mathbf{w})$ and $\\mathbf{F}_{\\mathbf{r}}(\\mathbf{r},\\mathbf{w})$\n",
+    "\n",
+    "where\n",
+    "  \n",
+    "  $$ \\mathbf{F}_{\\mathbf{r}}(\\mathbf{r},\\mathbf{w}) = \\begin{pmatrix} \n",
+    "\\frac{ \\partial \\mathcal{K}_{0}}{\\partial r_{0}}  & ... & \\frac{ \\partial \\mathcal{K}_{0}}{ \\partial r_{t}} & ... & \\frac{ \\partial  \\mathcal{K}_{0}}{\\partial r_{T}} \\\\ . \\\\ .\n",
+    "\\\\ \n",
+    ".\n",
+    "\\\\\n",
+    "\\frac{ \\partial \\mathcal{K}_{t}}{\\partial r_{0}}  & ... & \\frac{ \\partial \\mathcal{K}_{t}}{ \\partial r_{t}} & ... & \\frac{ \\partial \\mathcal{K}_{t}}{\\partial r_{T}} \\\\ . \\\\ .\n",
+    "\\\\ . \\\\\n",
+    "\\frac{ \\partial \\mathcal{K}_{T}}{\\partial r_{0}}  & ...  & \\frac{ \\partial \\mathcal{K}_{T}}{ \\partial r_{t}} & ... & \\frac{ \\partial  \\mathcal{K}_{T}}{\\partial r_{T}}  \\\\ \\\\\n",
+    " \\end{pmatrix} $$\n",
+    " \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1488f183",
+   "metadata": {},
+   "source": [
+    "- These Jacobian matrices are the most computationally complex object to compute in the model. Direct methods can take up to 20 minutes for each matrix.\n",
+    "\n",
+    "- The sequence space jacobian methodology proposes a 'fake news' algorithm to solve these matrices in under 3 seconds!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e585b986",
+   "metadata": {},
+   "source": [
+    "## Overall algorithm\n",
+    "\n",
+    "\n",
+    "1. Define model in sequence space\n",
+    "\n",
+    "$$\\mathbf{H}(\\mathbf{U},\\mathbf{Z}) = \\mathbf{0}$$\n",
+    "\n",
+    "2. Solve for steady state\n",
+    "\n",
+    "$$\\mathbf{H}(\\mathbf{U}_{ss},\\mathbf{Z}_{ss}) = \\mathbf{0}$$\n",
+    "\n",
+    "where $\\mathbf{U}_{ss} = \\left(U_{ss}, .. U_{ss}, .., U_{ss} \\right)$ \n",
+    "\n",
+    "3. Linearize around steady state and solve for impulse responses $d\\mathbf{U}$ given exogenous shock $d\\mathbf{Z}$\n",
+    "\n",
+    "$$ d\\mathbf{U} = -\\mathbf{H}_{\\mathbf{U}}^{-1} \\mathbf{H}_{\\mathbf{Z}} d\\mathbf{Z}$$\n",
+    "\n",
+    "- When linearizing , solve heterogenous agent jacobians with fake news algorithm\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98072c90",
+   "metadata": {},
+   "source": [
+    "## What if model contains many equations?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c66c3791",
+   "metadata": {},
+   "source": [
+    "The system above can be reduced to \n",
+    "\n",
+    "$$ H_{t}(\\mathbf{U},\\mathbf{Z}) = \\begin{pmatrix} \n",
+    " \\mathcal{K}_{t}(\\{\\alpha  Z_{t} K_{t}^{\\alpha - 1} \\bar{L}^{1-\\alpha} ,(1-\\alpha) Z_{s} K_{s}^{\\alpha} \\bar{L}^{-\\alpha} \\}_{s=0}^{s=T}) - K_{t}  \\\\ \\\\\n",
+    " \\end{pmatrix} = 0, \\quad t=0,1 ,2,3,....$$\n",
+    " \n",
+    " where  $\\mathbf{U} = \\left(K_{0},K_{1}.K_{2}....,K_{T}  \\right)$ \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82e7cfee",
+   "metadata": {},
+   "source": [
+    "When the model contains many equations, we can represent the model as a directed acyclic graph to determine how the model should be reduced. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0b15d547",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython import display\n",
+    "\n",
+    "display.Image(\"KS_DAG.jpeg\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/SSJ-Example/estimation/__init__.py b/examples/SSJ-Example/estimation/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/SSJ-Example/estimation/create_data.py b/examples/SSJ-Example/estimation/create_data.py
new file mode 100644
index 000000000..c35f278cb
--- /dev/null
+++ b/examples/SSJ-Example/estimation/create_data.py
@@ -0,0 +1,37 @@
+from pandas_datareader.fred import FredReader
+import pandas as pd
+
+start = "1966-01"
+end = "2004-12"
+
+# Define series to load from Fred
+series = {
+    "PCEPILFE": "pcecore",  # PCE deflator excl food and energy (index 2012) [M]
+    "GDPC1": "gdp",  # real GDP (index 2012)
+    "FEDFUNDS": "ffr",
+}  # Fed Funds rate
+
+# Load series from Fred
+df_fred = (
+    FredReader(series.keys(), start="1959-01").read().rename(series, axis="columns")
+)
+df_fred.index = df_fred.index.to_period("M")
+
+# make everything quarterly
+df_fred = df_fred.groupby(pd.PeriodIndex(df_fred.index, freq="Q")).mean()
+
+# start new dataframe into which we'll selectively load
+df = pd.DataFrame(index=df_fred.index)
+
+# Load series
+df["pi"] = 100 * (
+    (df_fred["pcecore"] / df_fred["pcecore"].shift(1)) ** 4 - 1
+)  # inflation is PCE
+df["Y"] = df_fred["gdp"]
+df["i"] = df_fred["ffr"]  # fed funds rate
+
+# only keep start to end and define time variable
+df = df[start:end]
+df.index.name = "t"
+
+df.to_csv("us_data.csv")
diff --git a/examples/SSJ-Example/estimation/model.py b/examples/SSJ-Example/estimation/model.py
new file mode 100644
index 000000000..d1b7dc487
--- /dev/null
+++ b/examples/SSJ-Example/estimation/model.py
@@ -0,0 +1,78 @@
+"""
+This HA model combines SIM, a Taylor rule for monetary policy
+with a gradual tax adjustment rule and real bonds on the fiscal side
+"""
+
+import sequence_jacobian as sj
+
+"""Simple household block"""
+
+hh = sj.hetblocks.hh_sim.hh
+make_grids = sj.hetblocks.hh_sim.make_grids
+
+
+def income(Y, T, e_grid):
+    # post-tax labor income
+    y = (Y - T) * e_grid
+    return y
+
+
+household_simple = hh.add_hetinputs([make_grids, income])
+
+
+"""Inflation, monetary, and fiscal"""
+
+
+@sj.simple
+def nkpc(pi, Y, X, C, kappa_w, vphi, frisch, markup_ss, eis, beta):
+    piw = pi + X - X(-1)
+    # note: for simplicity, we ignore distortionary effect of taxation here
+    piwres = (
+        kappa_w * (vphi * (Y / X) ** (1 / frisch) - 1 / markup_ss * X * C ** -(1 / eis))
+        + beta * piw(1)
+        - piw
+    )
+    return piwres, piw
+
+
+@sj.simple
+def monetary_taylor(pi, ishock, rss, phi_pi):
+    i = rss + phi_pi * pi + ishock
+    r_ante = i - pi(1)
+    return i, r_ante
+
+
+@sj.simple
+def ex_post_rate(r_ante):
+    r = r_ante(-1)
+    return r
+
+
+@sj.solved(unknowns={"B": (-1.0, 1.0)}, targets=["Bres"], solver="brentq")
+def fiscal_deficit_Trule(r, G, B, Tss, phi_T, Y):
+    T = Tss + phi_T * (B(-1) - B.ss)
+    Bres = (1 + r) * B(-1) + G - T - B
+    return T, Bres
+
+
+"""Overall model"""
+
+
+@sj.simple
+def mkt_clearing(A, B, Y, C, G):
+    asset_mkt = A - B
+    goods_mkt = C + G - Y
+    return asset_mkt, goods_mkt
+
+
+ha = sj.create_model(
+    [
+        household_simple,
+        nkpc,
+        monetary_taylor,
+        ex_post_rate,
+        fiscal_deficit_Trule,
+        mkt_clearing,
+    ],
+    name="Simple HA Model",
+)
diff --git a/examples/SSJ-Example/estimation/plots.py b/examples/SSJ-Example/estimation/plots.py
new file mode 100644
index 000000000..f68862d79
--- /dev/null
+++ b/examples/SSJ-Example/estimation/plots.py
@@ -0,0 +1,61 @@
+"""Simple plotting functions for time series"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def plot_timeseries(data_dict, dims, xlabel="Quarters", **kwargs):
+    plt.figure(**kwargs)
+    for i, (name, data) in enumerate(data_dict.items()):
+        plt.subplot(*dims, i + 1)
+        plt.plot(data)
+        plt.title(name)
+        plt.xlabel(xlabel)
+        plt.axhline(y=0, color="#808080", linestyle=":")
+    plt.tight_layout()
+
+
+def plot_impulses(
+    dict_of_impulsedicts, labels, series, dims, xlabel="Quarters", T=None, **kwargs
+):
+    plt.figure(**kwargs)
+    for i, name in enumerate(series):
+        plt.subplot(*dims, i + 1)
+        for k, impulse_dict in dict_of_impulsedicts.items():
+            plt.plot(impulse_dict.get(name)[:T], label=labels[k])
+        plt.title(name)
+        plt.xlabel(xlabel)
+        plt.axhline(y=0, color="#808080", linestyle=":")
+        if i == 0:
+            plt.legend()
+    plt.tight_layout()
+
+
+def plot_decomp(Ds, data, shocks, series, xaxis, **kwargs):
+    Tshow, len_se, len_sh = Ds.shape
+    plt.figure(**kwargs)
+
+    for io, o in enumerate(series):
+        plt.subplot(1, 3, 1 + io)
+        y_offset_pos, y_offset_neg = 0, 0
+
+        for ii, i in enumerate(shocks):
+            D = Ds[:, io, ii]  # current contribution
+            y_offset = (D > 0) * y_offset_pos + (D < 0) * y_offset_neg
+            y_offset_pos_ = y_offset_pos + np.maximum(D, 0)
+            y_offset_neg_ = y_offset_neg - np.maximum(-D, 0)
+            plt.fill_between(
+                xaxis, y_offset_pos, y_offset_pos_, color=f"C{ii}", label=i
+            )
+            plt.fill_between(xaxis, y_offset_neg, y_offset_neg_, color=f"C{ii}")
+            y_offset_pos = y_offset_pos_
+            y_offset_neg = y_offset_neg_
+
+        if data is not None:
+            plt.plot(xaxis, data[:, io], color="black")
+        if io == 0:
+            plt.legend(framealpha=1)
+        plt.title(o)
+
+    plt.tight_layout()
+    plt.show()
diff --git a/examples/SSJ-Example/estimation/routines.py b/examples/SSJ-Example/estimation/routines.py
new file mode 100644
index 000000000..11a280ab9
--- /dev/null
+++ b/examples/SSJ-Example/estimation/routines.py
@@ -0,0 +1,186 @@
+"""Core utilities for simulation, second moments, and estimation"""
+
+import numpy as np
+import numba
+
+"""Simulation"""
+
+
+def simulate(impulses, outputs, T_sim):
+    """
+    impulses: list of ImpulseDicts, each an impulse to independent unit normal shock
+    outputs: list of outputs we want in simulation
+    T_sim: length of simulation
+
+    simulation: dict mapping each output to length-T_sim simulated series
+    """
+
+    simulation = {}
+    epsilons = [np.random.randn(T_sim + impulses[0].T - 1) for _ in impulses]
+    for o in outputs:
+        simulation[o] = sum(
+            simul_shock(imp[o], eps) for imp, eps in zip(impulses, epsilons)
+        )
+
+    return simulation
+
+
+@numba.njit(parallel=True)
+def simul_shock(dX, epsilons):
+    """Take in any impulse response dX to epsilon shock, plus path of epsilons, and simulate"""
+    # if I have T_eps epsilons, can simulate length T_eps - T + 1 dXtildes
+    # by indexing as eps_(-T-1), ... , eps_(T_eps-T+1) and implementing formula
+    T = len(dX)
+    T_eps = len(epsilons)
+    dXtilde = np.empty(T_eps - T + 1)
+
+    dX_flipped = dX[::-1].copy()  # flip because dX_s multiplies eps_(t-s)
+    for t in numba.prange(T_eps - T + 1):
+        dXtilde[t] = np.vdot(
+            dX_flipped, epsilons[t : t + T]
+        )  # sum as single dot product
+
+    return dXtilde
+
+
+"""Log-likelihood of priors"""
+
+
+def log_priors(thetas, priors_list):
+    """Given a vector 'thetas', where entry i is drawn from the prior
+    distribution specified in entry i of priors_list, calculate sum of
+    log prior likelihoods of each theta. Distributions over theta should be specified
+    in the same way that arguments are given to the 'log_prior' function: first the
+    name of the family, and then two parameters"""
+    return sum(log_prior(theta, *prior) for theta, prior in zip(thetas, priors_list))
+
+
+def log_prior(theta, dist, arg1, arg2):
+    """Calculate log prior probability of 'theta', if prior is from family
+    'dist' with parameters 'arg1' and 'arg2' (depends on prior)"""
+    if dist == "Normal":
+        mu = arg1
+        sigma = arg2
+        return -0.5 * ((theta - mu) / sigma) ** 2
+    elif dist == "Uniform":
+        lb = arg1
+        ub = arg2
+        return -np.log(ub - lb)
+    elif dist == "Invgamma":
+        s = arg1
+        v = arg2
+        return (-v - 1) * np.log(theta) - v * s**2 / (2 * theta**2)
+    elif dist == "Gamma":
+        theta = arg2**2 / arg1
+        k = arg1 / theta
+        return (k - 1) * np.log(theta) - theta / theta
+    elif dist == "Beta":
+        alpha = (arg1 * (1 - arg1) - arg2**2) / (arg2**2 / arg1)
+        beta = alpha / arg1 - alpha
+        return (alpha - 1) * np.log(theta) + (beta - 1) * np.log(1 - theta)
+    else:
+        raise ValueError("Distribution provided is not implemented in log_prior!")
+
+
+"""Historical decomposition"""
+
+
+def back_out_shocks(As, y, sigma_e=None, sigma_o=None, preperiods=0):
+    """Calculates most likely shock paths if As is true set of IRFs
+
+    Parameters
+    ----------
+    As : array (Tm*O*E) giving the O*E matrix mapping shocks to observables at each of Tm lags in the MA(infty),
+            e.g. As[6, 3, 5] gives the impact of shock 5, 6 periods ago, on observable 3 today
+    y : array (To*O) giving the data (already assumed to be demeaned, though no correction is made for this in the log-likelihood)
+            each of the To rows t is the vector of observables at date t (earliest should be listed first)
+    sigma_e : [optional] array (E) giving sd of each shock e, assumed to be 1 if not provided
+    sigma_o : [optional] array (O) giving sd of iid measurement error for each observable o, assumed to be 0 if not provided
+    preperiods : [optional] integer number of pre-periods during which we allow for shocks too. This is suggested to be at
+            least 1 in models where some variables (e.g. investment) only respond with a 1 period lag.
+            (Otherwise there can be invertibility issues)
+
+    Returns
+    ----------
+    eps_hat : array (To*E) giving most likely path of all shocks
+    Ds : array (To*O*E) giving the level of each observed data series that is accounted for by each shock
+    """
+    # Step 1: Rescale As any y
+    To, Oy = y.shape
+    Tm, O, E = As.shape
+    assert Oy == O
+    To_with_pre = To + preperiods
+
+    A_full = construct_stacked_A(
+        As, To=To_with_pre, To_out=To, sigma_e=sigma_e, sigma_o=sigma_o
+    )
+    if sigma_o is not None:
+        y = y / sigma_o
+    y = y.reshape(To * O)
+
+    # Step 2: Solve OLS
+    eps_hat = np.linalg.lstsq(A_full, y, rcond=None)[
+        0
+    ]  # this is To*E x 1 dimensional array
+    eps_hat = eps_hat.reshape((To_with_pre, E))
+
+    # Step 3: Decompose data
+    for e in range(E):
+        A_full = A_full.reshape((To, O, To_with_pre, E))
+        Ds = np.sum(A_full * eps_hat, axis=2)
+
+    # Cut away pre periods from eps_hat
+    eps_hat = eps_hat[preperiods:, :]
+
+    return eps_hat, Ds
+
+
+def construct_stacked_A(
+    As, To, To_out=None, sigma_e=None, sigma_o=None, reshape=True, long=False
+):
+    Tm, O, E = As.shape
+
+    # how long should the IRFs be that we stack in A_full?
+    if To_out is None:
+        To_out = To
+    if long:
+        To_out = To + Tm  # store even the last shock's IRF in full!
+
+    # allocate memory for A_full
+    A_full = np.zeros((To_out, O, To, E))
+
+    for o in range(O):
+        for itshock in range(To):
+            # if To > To_out, allow the first To - To_out shocks to happen before the To_out time periods
+            if To <= To_out:
+                iA_full = itshock
+                iAs = 0
+
+                shock_length = min(Tm, To_out - iA_full)
+            else:
+                # this would be the correct start time of the shock
+                iA_full = itshock - (To - To_out)
+
+                # since it can be negative, only start IRFs at later date
+                iAs = -min(iA_full, 0)
+
+                # correct iA_full by that date
+                iA_full += -min(iA_full, 0)
+
+                shock_length = min(Tm, To_out - iA_full)
+
+            for e in range(E):
+                A_full[iA_full : iA_full + shock_length, o, itshock, e] = As[
+                    iAs : iAs + shock_length, o, e
+                ]
+                if sigma_e is not None:
+                    A_full[iA_full : iA_full + shock_length, o, itshock, e] *= sigma_e[
+                        e
+                    ]
+                if sigma_o is not None:
+                    A_full[iA_full : iA_full + shock_length, o, itshock, e] /= sigma_o[
+                        o
+                    ]
+    if reshape:
+        A_full = A_full.reshape((To_out * O, To * E))
+    return A_full
diff --git a/examples/SSJ-Example/estimation/us_data.csv b/examples/SSJ-Example/estimation/us_data.csv
new file mode 100644
index 000000000..28bd42342
--- /dev/null
+++ b/examples/SSJ-Example/estimation/us_data.csv
@@ -0,0 +1,157 @@
+t,pi,Y,i
+1966Q1,2.027726462326318,4409.518,4.56
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+1991Q4,3.1836329258542184,9427.581,4.816666666666666
+1992Q1,3.09644363023045,9540.444,4.023333333333333
+1992Q2,2.8563887638782814,9643.893,3.7699999999999996
+1992Q3,2.390044216982856,9739.185,3.2566666666666664
+1992Q4,2.964125852679911,9840.753,3.0366666666666666
+1993Q1,2.783004173985537,9857.185,3.0399999999999996
+1993Q2,3.07071009671529,9914.565,3.0
+1993Q3,2.2556908169596435,9961.873,3.06
+1993Q4,2.0744170484439106,10097.362,2.99
+1994Q1,1.8284621988157612,10195.338,3.2133333333333334
+1994Q2,2.719689723611274,10333.495,3.94
+1994Q3,2.2529429759334585,10393.898,4.486666666666667
+1994Q4,2.0457251419062183,10512.962,5.166666666666667
+1995Q1,2.112723917077197,10550.251,5.81
+1995Q2,2.395348900058236,10581.723,6.02
+1995Q3,1.9233521188806169,10671.738,5.796666666666667
+1995Q4,2.0210880472447235,10744.203,5.72
+1996Q1,1.5786404616660477,10824.674,5.363333333333333
+1996Q2,1.9167858345777278,11005.217,5.243333333333333
+1996Q3,1.8379638284230282,11103.935,5.306666666666667
+1996Q4,2.2961949165604123,11219.238,5.28
+1997Q1,1.5806884724582915,11291.665,5.276666666666666
+1997Q2,2.1028090646867836,11479.33,5.523333333333333
+1997Q3,0.9365140059810262,11622.911,5.533333333333334
+1997Q4,1.2733258670848402,11722.722,5.506666666666667
+1998Q1,1.1825584715647208,11839.876,5.52
+1998Q2,1.230948099766982,11949.492,5.5
+1998Q3,1.4290518121450324,12099.191,5.533333333333334
+1998Q4,1.277794248941766,12294.737,4.86
+1999Q1,1.0101467687091858,12410.778,4.733333333333333
+1999Q2,1.3953198458593885,12514.408,4.746666666666667
+1999Q3,1.480837465298146,12679.977,5.093333333333334
+1999Q4,1.8940495006775304,12888.281,5.306666666666667
+2000Q1,2.1126402624124463,12935.252,5.676666666666667
+2000Q2,1.4035489543970048,13170.749,6.273333333333333
+2000Q3,1.9409855161090617,13183.89,6.52
+2000Q4,1.9985997069409622,13262.25,6.473333333333334
+2001Q1,2.6251602936705476,13219.251,5.593333333333334
+2001Q2,1.5195002008354441,13301.394,4.326666666666667
+2001Q3,1.0378460852063842,13248.142,3.4966666666666666
+2001Q4,1.943387349712089,13284.881,2.1333333333333333
+2002Q1,1.23520367779284,13394.91,1.7333333333333334
+2002Q2,2.2204300258506393,13477.356,1.75
+2002Q3,2.14639049932428,13531.741,1.74
+2002Q4,1.4302514400940325,13549.421,1.4433333333333334
+2003Q1,1.2409818612027612,13619.434,1.25
+2003Q2,1.5262288140348357,13741.107,1.2466666666666668
+2003Q3,1.8425461114967678,13970.157,1.0166666666666666
+2003Q4,1.803906908203734,14131.379,0.9966666666666667
+2004Q1,2.2187632117856237,14212.34,1.0033333333333332
+2004Q2,2.3043794032079568,14323.017,1.01
+2004Q3,1.4414629413773028,14457.832,1.4333333333333333
+2004Q4,2.1859911994692727,14605.595,1.95
diff --git a/requirements/base.txt b/requirements/base.txt
index a3a4d34ce..389418f02 100644
--- a/requirements/base.txt
+++ b/requirements/base.txt
@@ -8,4 +8,5 @@ pandas>=1.5
 quantecon
 scipy>=1.10
 seaborn>=0.12
+sequence-jacobian
 xarray>=2023


Test patch:
diff --git a/HARK/ConsumptionSaving/tests/test_ConsNewKeynesianModel.py b/HARK/ConsumptionSaving/tests/test_ConsNewKeynesianModel.py
new file mode 100644
index 000000000..10a75e2c5
--- /dev/null
+++ b/HARK/ConsumptionSaving/tests/test_ConsNewKeynesianModel.py
@@ -0,0 +1,101 @@
+import unittest
+
+import numpy as np
+
+from HARK.ConsumptionSaving.ConsNewKeynesianModel import NewKeynesianConsumerType
+from HARK.tests import HARK_PRECISION
+
+jacobian_test_dict = {
+    # Parameters shared with the perfect foresight model
+    "CRRA": 2,  # Coefficient of relative risk aversion
+    "Rfree": 1.04**0.25,  # Interest factor on assets
+    "DiscFac": 0.9735,  # Intertemporal discount factor
+    "LivPrb": [0.99375],  # Survival probability
+    "PermGroFac": [1.00],  # Permanent income growth factor
+    # Parameters that specify the income distribution over the lifecycle
+    "PermShkStd": [
+        0.06
+    ],  # [(0.01*4/11)**0.5],    # Standard deviation of log permanent shocks to income
+    "PermShkCount": 5,  # Number of points in discrete approximation to permanent income shocks
+    "TranShkStd": [0.3],  # Standard deviation of log transitory shocks to income
+    "TranShkCount": 5,  # Number of points in discrete approximation to transitory income shocks
+    "UnempPrb": 0.07,  # Probability of unemployment while working
+    "IncUnemp": 0.3,  # Unemployment benefits replacement rate
+    "UnempPrbRet": 0.0005,  # Probability of "unemployment" while retired
+    "IncUnempRet": 0.0,  # "Unemployment" benefits when retired
+    "T_retire": 0,  # Period of retirement (0 --> no retirement)
+    # Parameters for constructing the "assets above minimum" grid
+    "aXtraMin": 0.001,  # Minimum end-of-period "assets above minimum" value
+    "aXtraMax": 20,  # Maximum end-of-period "assets above minimum" value
+    "aXtraCount": 48,  # Number of points in the base grid of "assets above minimum"
+    "aXtraNestFac": 3,  # Exponential nesting factor when constructing "assets above minimum" grid
+    "aXtraExtra": None,  # Additional values to add to aXtraGrid
+    # A few other parameters
+    "BoroCnstArt": 0.0,  # Artificial borrowing constraint; imposed minimum level of end-of period assets
+    "vFuncBool": True,  # Whether to calculate the value function during solution
+    "CubicBool": False,  # Preference shocks currently only compatible with linear cFunc
+    "T_cycle": 1,  # Number of periods in the cycle for this agent type
+    # Parameters only used in simulation
+    "AgentCount": 500,  # Number of agents of this type
+    "T_sim": 100,  # Number of periods to simulate
+    "aNrmInitMean": np.log(1.3) - (0.5**2) / 2,  # Mean of log initial assets
+    "aNrmInitStd": 0.5,  # Standard deviation of log initial assets
+    "pLvlInitMean": 0,  # Mean of log initial permanent income
+    "pLvlInitStd": 0,  # Standard deviation of log initial permanent income
+    "PermGroFacAgg": 1.0,  # Aggregate permanent income growth factor
+    "T_age": None,  # Age after which simulated agents are automatically killed
+    # Parameters for Transition Matrix Simulation
+    "mMin": 0.001,
+    "mMax": 20,
+    "mCount": 48,
+    "mFac": 3,
+}
+
+
+# %% Test Transition Matrix Methods
+
+
+class test_Transition_Matrix_Methods(unittest.TestCase):
+    def test_calc_tran_matrix(self):
+        example1 = NewKeynesianConsumerType(**jacobian_test_dict)
+        example1.cycles = 0
+        example1.solve()
+
+        example1.define_distribution_grid()
+        p = example1.dist_pGrid  # Grid of permanent income levels
+
+        example1.calc_transition_matrix()
+        c = example1.cPol_Grid  # Normalized Consumption Policy Grid
+        asset = example1.aPol_Grid  # Normalized Asset Policy Grid
+
+        example1.calc_ergodic_dist()
+        vecDstn = example1.vec_erg_dstn
+        # Distribution of market resources and permanent income as a vector (m*p)x1 vector where
+
+        # Compute Aggregate Consumption and Aggregate Assets
+        gridc = np.zeros((len(c), len(p)))
+        grida = np.zeros((len(asset), len(p)))
+
+        for j in range(len(p)):
+            gridc[:, j] = p[j] * c  # unnormalized Consumption policy grid
+            grida[:, j] = p[j] * asset  # unnormalized Asset policy grid
+
+        AggC = np.dot(gridc.flatten(), vecDstn)  # Aggregate Consumption
+        AggA = np.dot(grida.flatten(), vecDstn)  # Aggregate Assets
+
+        self.assertAlmostEqual(AggA[0], 1.19513, places=4)
+        self.assertAlmostEqual(AggC[0], 1.00417, places=4)
+
+
+# %% Test Heterogenous Agent Jacobian Methods
+
+
+class test_Jacobian_methods(unittest.TestCase):
+    def test_calc_jacobian(self):
+        Agent = NewKeynesianConsumerType(**jacobian_test_dict)
+        Agent.compute_steady_state()
+        CJAC_Perm, AJAC_Perm = Agent.calc_jacobian("PermShkStd", 50)
+
+        self.assertAlmostEqual(CJAC_Perm.T[30][29], -0.06120, places=HARK_PRECISION)
+        self.assertAlmostEqual(CJAC_Perm.T[30][30], 0.05307, places=HARK_PRECISION)
+        self.assertAlmostEqual(CJAC_Perm.T[30][31], 0.04674, places=HARK_PRECISION)
diff --git a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py
index 1b8113add..7f8e0a421 100644
--- a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py
+++ b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py
@@ -836,54 +836,3 @@ def test_compare_t_age_t_cycle_premature_death(self):
         # (the exception from before should not happen
         # because we are killing agents before T_cycle)
         self.assertTrue(np.all(hist["t_age"] == hist["t_cycle"]))
-
-
-# %% Test Transition Matrix Methods
-
-
-class test_Transition_Matrix_Methods(unittest.TestCase):
-    def test_calc_tran_matrix(self):
-        example1 = IndShockConsumerType(**dict_harmenberg)
-        example1.cycles = 0
-        example1.solve()
-
-        example1.define_distribution_grid()
-        p = example1.dist_pGrid  # Grid of permanent income levels
-
-        example1.calc_transition_matrix()
-        c = example1.cPol_Grid  # Normalized Consumption Policy Grid
-        asset = example1.aPol_Grid  # Normalized Asset Policy Grid
-
-        example1.calc_ergodic_dist()
-        vecDstn = example1.vec_erg_dstn
-        # Distribution of market resources and permanent income as a vector (m*p)x1 vector where
-
-        # Compute Aggregate Consumption and Aggregate Assets
-        gridc = np.zeros((len(c), len(p)))
-        grida = np.zeros((len(asset), len(p)))
-
-        for j in range(len(p)):
-            gridc[:, j] = p[j] * c  # unnormalized Consumption policy grid
-            grida[:, j] = p[j] * asset  # unnormalized Asset policy grid
-
-        AggC = np.dot(gridc.flatten(), vecDstn)  # Aggregate Consumption
-        AggA = np.dot(grida.flatten(), vecDstn)  # Aggregate Assets
-
-        self.assertAlmostEqual(AggA[0], 1.19513, places=4)
-        self.assertAlmostEqual(AggC[0], 1.00417, places=4)
-
-
-# %% Test Heterogenous Agent Jacobian Methods
-
-
-class test_Jacobian_methods(unittest.TestCase):
-    def test_calc_jacobian(self):
-        Agent = IndShockConsumerType(**dict_harmenberg)
-
-        Agent.compute_steady_state()
-
-        CJAC_Perm, AJAC_Perm = Agent.calc_jacobian("PermShkStd", 50)
-
-        self.assertAlmostEqual(CJAC_Perm.T[30][29], -0.06120, places=HARK_PRECISION)
-        self.assertAlmostEqual(CJAC_Perm.T[30][30], 0.05307, places=HARK_PRECISION)
-        self.assertAlmostEqual(CJAC_Perm.T[30][31], 0.04674, places=HARK_PRECISION)