# Buggy code
def get_prediction(
    self, start=None, end=None, dynamic=False, index=None, exog=None, **kwargs
):
    """
    In-sample prediction and out-of-sample forecasting

    Parameters
    ----------
    start : int, str, or datetime, optional
        Zero-indexed observation number at which to start forecasting, ie.,
        the first forecast is start. Can also be a date string to
        parse or a datetime type. Default is the the zeroth observation.
    end : int, str, or datetime, optional
        Zero-indexed observation number at which to end forecasting, ie.,
        the first forecast is start. Can also be a date string to
        parse or a datetime type. However, if the dates index does not
        have a fixed frequency, end must be an integer index if you
        want out of sample prediction. Default is the last observation in
        the sample.
    exog : array_like, optional
        If the model includes exogenous regressors, you must provide
        exactly enough out-of-sample values for the exogenous variables if
        end is beyond the last observation in the sample.
    dynamic : bool, int, str, or datetime, optional
        Integer offset relative to `start` at which to begin dynamic
        prediction. Can also be an absolute date string to parse or a
        datetime type (these are not interpreted as offsets).
        Prior to this observation, true endogenous values will be used for
        prediction; starting with this observation and continuing through
        the end of prediction, forecasted endogenous values will be used
        instead.
    full_results : bool, optional
        If True, returns a FilterResults instance; if False returns a
        tuple with forecasts, the forecast errors, and the forecast error
        covariance matrices. Default is False.
    **kwargs
        Additional arguments may required for forecasting beyond the end
        of the sample. See `FilterResults.predict` for more details.

    Returns
    -------
    forecast : array
        Array of out of sample forecasts.
    """
    if start is None:
        if (
            self.model.simple_differencing
            and not self.model._index_generated
            and not self.model._index_dates
        ):
            start = 0
        else:
            start = self.model._index[0]

    # Handle start, end, dynamic
    _start, _end, _out_of_sample, prediction_index = self.model._get_prediction_index(
        start, end, index, silent=True
    )

    # Handle exogenous parameters
    if _out_of_sample and (self.model.k_exog + self.model.k_trend > 0):
        # Create a new faux SARIMAX model for the extended dataset
        nobs = self.model.data.orig_endog.shape[0] + _out_of_sample
        endog = np.zeros((nobs, self.model.k_endog))

        if self.model.k_exog > 0:
            if exog is None:
                raise ValueError(
                    "Out-of-sample forecasting in a model"
                    " with a regression component requires"
                    " additional exogenous values via the"
                    " `exog` argument."
                )
            exog = np.array(exog)
            required_exog_shape = (_out_of_sample, self.model.k_exog)
            try:
                exog = exog.reshape(required_exog_shape)
            except ValueError:
                raise ValueError(
                    "Provided exogenous values are not of the"
                    " appropriate shape. Required %s, got %s."
                    % (str(required_exog_shape), str(exog.shape))
                )
            exog = np.c_[self.model.data.orig_exog.T, exog.T].T

        model_kwargs = self._init_kwds.copy()
        model_kwargs["exog"] = exog
        model = SARIMAX(endog, **model_kwargs)
        model.update(self.params, transformed=True, includes_fixed=True)

        # Set the kwargs with the update time-varying state space
        # representation matrices
        for name in self.filter_results.shapes.keys():
            if name == "obs":
                continue
            mat = getattr(model.ssm, name)
            if mat.shape[-1] > 1:
                kwargs[name] = mat[..., -_out_of_sample:]
    elif self.model.k_exog == 0 and exog is not None:
        warn(
            "Exogenous array provided to predict, but additional data not"
            " required. `exog` argument ignored.",
            ValueWarning,
        )

    return super(SARIMAXResults, self).get_prediction(
        start=start, end=end, dynamic=dynamic, index=index, exog=exog, **kwargs
    )


# Traceback (IndexError)
c:\git\statsmodels\statsmodels\tsa\statespace\sarimax.py:914: RuntimeWarning: overflow encountered in square
params_variance = (residuals[k_params_ma:]**2).mean()
c:\git\statsmodels\statsmodels\tsa\statespace\sarimax.py:976: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
c:\git\statsmodels\statsmodels\tsa\statespace\tools.py:487: RuntimeWarning: invalid value encountered in double_scalars
y[k-1, i] = (y[k, i] - y[k, k]*y[k, k-i-1]) / (1 - y[k, k]**2)
c:\git\statsmodels\statsmodels\tsa\statespace\tools.py:489: RuntimeWarning: invalid value encountered in sqrt
x = r / ((1 - r**2)**0.5)
Traceback (most recent call last):
File "C:\Anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3326, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-8-a7c5e4baeb4f>", line 10, in <module>
res = mod.fit()
File "c:\git\statsmodels\statsmodels\tsa\statespace\mlemodel.py", line 605, in fit
start_params = self.untransform_params(start_params)
File "c:\git\statsmodels\statsmodels\tsa\statespace\sarimax.py", line 1495, in untransform_params
unconstrained[start] = constrained[start]**0.5
IndexError: index 6 is out of bounds for axis 0 with size 4

# Fixed code
def get_prediction(
    self, start=None, end=None, dynamic=False, index=None, exog=None, **kwargs
):
    r"""
    In-sample prediction and out-of-sample forecasting

    Parameters
    ----------
    start : int, str, or datetime, optional
        Zero-indexed observation number at which to start forecasting, ie.,
        the first forecast is start. Can also be a date string to
        parse or a datetime type. Default is the the zeroth observation.
    end : int, str, or datetime, optional
        Zero-indexed observation number at which to end forecasting, ie.,
        the first forecast is start. Can also be a date string to
        parse or a datetime type. However, if the dates index does not
        have a fixed frequency, end must be an integer index if you
        want out of sample prediction. Default is the last observation in
        the sample.
    exog : array_like, optional
        If the model includes exogenous regressors, you must provide
        exactly enough out-of-sample values for the exogenous variables if
        end is beyond the last observation in the sample.
    dynamic : bool, int, str, or datetime, optional
        Integer offset relative to `start` at which to begin dynamic
        prediction. Can also be an absolute date string to parse or a
        datetime type (these are not interpreted as offsets).
        Prior to this observation, true endogenous values will be used for
        prediction; starting with this observation and continuing through
        the end of prediction, forecasted endogenous values will be used
        instead.
    full_results : bool, optional
        If True, returns a FilterResults instance; if False returns a
        tuple with forecasts, the forecast errors, and the forecast error
        covariance matrices. Default is False.
    **kwargs
        Additional arguments may required for forecasting beyond the end
        of the sample. See `FilterResults.predict` for more details.

    Returns
    -------
    forecast : array
        Array of out of sample forecasts.
    """
    if start is None:
        if (
            self.model.simple_differencing
            and not self.model._index_generated
            and not self.model._index_dates
        ):
            start = 0
        else:
            start = self.model._index[0]

    # Handle start, end, dynamic
    _start, _end, _out_of_sample, prediction_index = self.model._get_prediction_index(
        start, end, index, silent=True
    )

    # Handle exogenous parameters
    if _out_of_sample and (self.model.k_exog + self.model.k_trend > 0):
        # Create a new faux SARIMAX model for the extended dataset
        nobs = self.model.data.orig_endog.shape[0] + _out_of_sample
        endog = np.zeros((nobs, self.model.k_endog))

        if self.model.k_exog > 0:
            if exog is None:
                raise ValueError(
                    "Out-of-sample forecasting in a model"
                    " with a regression component requires"
                    " additional exogenous values via the"
                    " `exog` argument."
                )
            exog = np.array(exog)
            required_exog_shape = (_out_of_sample, self.model.k_exog)
            try:
                exog = exog.reshape(required_exog_shape)
            except ValueError:
                raise ValueError(
                    "Provided exogenous values are not of the"
                    " appropriate shape. Required %s, got %s."
                    % (str(required_exog_shape), str(exog.shape))
                )
            exog = np.c_[self.model.data.orig_exog.T, exog.T].T

        model_kwargs = self._init_kwds.copy()
        model_kwargs["exog"] = exog
        model = SARIMAX(endog, **model_kwargs)
        model.update(self.params, transformed=True, includes_fixed=True)

        # Set the kwargs with the update time-varying state space
        # representation matrices
        for name in self.filter_results.shapes.keys():
            if name == "obs":
                continue
            mat = getattr(model.ssm, name)
            if mat.shape[-1] > 1:
                kwargs[name] = mat[..., -_out_of_sample:]
    elif self.model.k_exog == 0 and exog is not None:
        warn(
            "Exogenous array provided to predict, but additional data not"
            " required. `exog` argument ignored.",
            ValueWarning,
        )

    return super(SARIMAXResults, self).get_prediction(
        start=start, end=end, dynamic=dynamic, index=index, exog=exog, **kwargs
    )