PRI Discussion Paper Series (No.20A-14)

DSGE Models Used by Policymakers: A Survey

Policy Research Institute, Ministry of Finance, Japan

Takeshi Yagihashi

October 2020

The views expressed in this paper are those of theauthors and not those of the Ministry of Finance or
the Policy Research Institute.

Research Department Policy Research Institute, MOF
3-1-1 Kasumigaseki, Chiyoda-ku, Tokyo 100-8940, JapanTEL 03-3581-4111

DSGE Models Used by Policymakers:
A Survey1
Takeshi Yagihashi2
Policy Research Institute, Ministry of Finance, Japan
October 2, 2020

Abstract:
The paper conducts a comprehensive survey on the current state of the dynamic stochastic generalequilibrium (DSGE) models developed by policy institutions, including central banks, government
agencies, and international organizations around the world. Our main sample consists of 84 models
developed by 58 institutions, and many of them were developed or updated after the 2008 financial
crisis. We first document the evolution of macroeconomic models used for policy purpose, andthen provide summary statistics on the models by type of institution, region, and number of authorsof the publication. We find that there is a steady increase in the development of DSGE models by
policy institutions. While central banks have been the main users of DSGE models, moregovernment agencies in Europe have been actively developing their own DSGE models in theyears following the 2008 Global Financial Crisis. We also find that some institutions have multiple
DSGE models serving different purposes. Next, we narrow our focus to a subset of 42 models thatare owned and actively used by policy institutions, and conduct a model comparison based on five
key model features. Although the models share common basic structures, there are large variations
in parameter values and modelling strategies, some of which do not necessarily reflect the findingsof the empirical literature. Finally, we create a score card for each model depending on whether
the model incorporated recent empirical findings on the five model features. Two models have ascore of 4 out of 5, and the overall average is 2.21. In conclusion, there is a greater need for future
DSGE policy models to adopt more recent findings in the empirical literature.

Keywords: DSGE model, financial friction, intertemporal elasticity of substitution, nonRicardian household

1
The analysis and conclusions are provided by the authors and do not reflect the views of the Ministry of Finance, Japan ormembers of its staff.
2
Address correspondence to: 3-1-1 Kasumigaseki, Chiyoda, Tokyo; email: takeshi.yagihashi@mof.go.jp.

01. Introduction
In recent years, a broad consensus has emerged among the academics that different typesof macroeconomic models should be used for different purposes (Blanchard, 2018). In the fieldof macroeconomic policymaking, central banks, government agencies such as the Ministry of
Finance, and international organizations have been the front runners in developing the dynamic
stochastic general equilibrium (DSGE) models. The DSGE modelling practice has received
many critiques from both researchers as well as practitioners following the Global Financial
Crisis of 2008 and the Euro Crisis of 2010 because most of them did not have model structuresthat enable them to analyze the effect of the crises and subsequent policy responses. Since thenmany policy institutions have responded by applying modifications to their models, such as
inclusion of unemployment, financial friction, and unconventional monetary policy, among
others. Policy institutions have been increasingly using the DSGE model as one of their mostimportant policy analysis tools to this day.
This paper’s main objective is to summarize the current state of the DSGE models
developed by policy institutions to obtain an updated perspective on “what is happening on the
ground” in the years after the Global Financial Crisis of 2008. To this end, we first collected
information on 84 DSGE models, three quarters of which were developed after the Global
Financial Crisis. Second, we provided basic descriptive statistics, such as the type andgeographical location of institutions involved, stated objectives of model development, numberof models held by each institution, and the number of the authors involved. Third, we conducted
model comparison using a subset of 42 models based on five household-related model features,
including intertemporal elasticity of substitution for consumption, habit formation, household
heterogeneity, financial friction, and labor supply elasticity. In our view, these five features1

capture the main differences across existing DSGE models and crucially affect model
performance that are related to monetary and fiscal policy.3
There are several key findings. First, while central banks are the main users of DSGE
models in the context of policymaking, in the years following the 2008 Global Financial Crisis,
more government agencies in Europe became involved in the development of this class ofmodels. Second, two-fifths of the institutions in our sample have more than one DSGE model,
each tailored to different objectives. Third, the number of the authors involved in thedevelopment of DSGE models has been rising, indicating that the models may have become
more complex over time. Fourth, we find large variations in the parameterization of key
parameters, which do not necessarily reflect the findings of the corresponding empiricalliterature. Some parameter choices appear to overstate the effectiveness of both monetary andfiscal policy. Finally, we create our own score card for each model based on whether the model
has incorporated the recent empirical findings on the five model features. None of the policy
models we examine receive the maximum score, with two of the models having a score of 4 outof 5. Indeed, the overall average is only 2.21, which suggests that much work remains to be donein connecting modelling practice in public institutions with more recent findings in the empiricalliterature.
There are a few survey studies on DSGE models used by public institutions, but they
focus on somewhat different issues from ours. For example, Kilponen et al. (2019) discuss how

3 More formally, the reasons for focusing on these household-related features can be summarized as follows. First, in the DSGE
literature, social welfare is often defined as a function of households’ consumption and leisure (or labor) summed over a lifetime.
Thus, understanding how households adjust their consumption and labor to a policy change is crucial in policy analysis. Second,the values of household-related parameters often differ by the type of data and methods used to pin down the parameter values.
For policy analysis to be credible, it is important that the adopted parameter values fall within a plausible range of the findings inacademic studies. Finally, how we model households’ decision on portfolio allocation (e.g., bank deposit vs stock) and the costs
associated with such decisions plays an important role in determining the real-world relevance of the credit channel of monetaryand fiscal policy.

2

DSGE models used by a few policy institutions differ from one another in model structure, while
Hall et al. (2013) document the history and roles of macroeconomic models (including a DSGE
model) used by public institutions. Compared to these studies, our contribution to the literature isthreefold. First, our study encompasses an unprecedented large number of DSGE policy models(= 84) developed by 58 institutions, many of which were developed or updated after the Global
Financial Crisis.4 Second, since we incorporated a large number of models and institutions, weare able to summarize the evolution of DSGE policy models over time and across institution typeand geographic region. Third, we provide a detailed comparison of model parameters related tothe households by using distributions of adopted parameter values. We also discuss whether theparameter choices reflect the recent empirical literature.
The paper is constructed as follows. In the next section, we provide some background on
DSGE models and the evolution of macroeconomic models used by policy institutions over time.
In the third section, we describe our sample selection criterion. The fourth section provides
descriptive statistics of the full sample, and the fifth section presents model comparison results
using a subset of models. The sixth section concludes.

2. Background
2.1 DSGE Models Used in Academia
The main characteristics of DSGE models is that it is “micro-founded,” i.e., agents
optimize their behavior based on well-specified and time-invariant preference and technology
parameters. The original motivation of developing such a model was to overcome the famous
critique by Robert E. Lucas Jr. (Lucas, 1976). Back in the 1970s, the prevailing models used in

4

The largest sample size in previous studies that the author is aware of is 37 by Sergi (2017).

3

analyzing the aggregate economy were built upon hundreds of reduced-form behavioral
equations that were estimated individually. Lucas argued that policy simulations based on thistype of model did not consider the possibility that the model parameters could vary in response
to a policy shift.
Lucas and his followers hoped that by building a micro-founded model, policy analysis
can be done in a clean lab-like environment. Since then, DSGE models gradually evolved fromthe simple, real business cycle model of Kydland and Prescott (1982) to the more elaborate“medium-scale” version that features multiple shocks and frictions (e.g., see Christiano et al.,2005 and Smets and Wouters, 2007). After the 2000s, the parameterization strategy also evolved
from a simple calibration exercise to a more elaborate estimation that incorporates the Bayesian
method. Meanwhile, the macroeconomic models that Lucas criticized earlier largely disappeared
within the academic community by the early 2000s.

2.2 Types of Macro Models Used by Policy Institutions
While DSGE models quickly gained popularity among academics during the 1980s and1990s, it is not until the 2000s that policymakers started to develop their own DSGE models. Even
when DSGE models were adopted by policymakers within their institution, they were often used
together with other types of models. According to Fukac and Pagan (2016), existing
macroeconomic models can be broadly divided into four generations, i.e., “1G,” “2G,” “3G,” and“4G” models. For simplicity, we re-classify them as “traditional macroeconometric models” (their
1G and 2G models combined), the “projection models” (their 3G model), and “DSGE policy
models” (their 4G model).5 We intentionally insert the term “policy” in between “DSGE” and5
The main distinction between 1G and 2G models is whether the error-correction mechanism is introduced or not, which is not
crucial for our analysis. 3G models are designed for short-term forecasting. They share some features with DSGE models used in4

“model” to emphasize that these models are internally developed and owned by policy institutions.
We will discuss in more detail how we judge whether a model is owned by an institution later.
Figure 1 illustrates the evolution of the macroeconomic models used by policy institutions.
This figure extends Pagan (2003)’s “best practice frontier,” a concept that is widely shared within
the policy community. The vertical axis in the figure represents how much a model honors thetheoretical foundation (“theoretical coherence”) while the horizontal axis represents how well themodel fits the data (“empirical coherence”). The oval represents the cluster of macroeconomic
models developed by policy institutions. There are two important points worth noting. First, weobserve that while the traditional macroeconometric models (1G and 2G models) start out
relatively high in terms of empirical coherence, later generation models (3G and 4G models) havebecome more “balanced” in terms of theoretical and empirical coherence. Second, the area coveredby the model cluster grows larger in size, which reflects the fact that different models have beendeveloped to meet a wider variety of policy objectives over time.6

2.3 Comparison Between DSGE and Non-DSGE Policy Models
Macroeconomic models used in policy institutions are generally tasked to do two things:
forecasting and policy analysis. Each institution places different weights on them based on
institutional mandate and the history of model development.

academia (stock-flow consistency, rule-based policy, presence of steady-state, multiple economic shocks, and the expectationaugmented Phillips curve). 4G models differ from 3G models in that a) economic shocks are explicitly modeled as an integral partof the model, b) adjustment cost appears directly in the primary objective functions, c) structural equations are often kept in the
Euler equation form, d) solution method is shaped to account for the possible shock, and e) several types of heterogeneity are
introduced in the model (Fukac and Pagan, 2016).
6 For example, the Italian Ministry of Economy and Finance use three variants of DSGE models (IGEM, IGEM2, and IGEM-PA)
to analyze different policy/forecasting issues. Bank of England possesses over 50 “suite models,” which cater to its flagship DSGE
model (COMPASS) in producing institutional forecast (Burgess et al., 2013). Further details on the objective of the DSGE policy
model will be assessed in Section 4.

5

Table 1 summarizes the strengths and weaknesses of three types of macroeconomic models
used by policy institutions based on the discussion of Hjelm et al. (2015). DSGE policy models
have a clear advantage in policy analysis because of the strict micro-foundation that was meant toovercome the aforementioned Lucas Critique. Projections models have an advantage in short-term
conditional forecasting, whereas traditional macroeconometric models are more suited for
conducting sectoral analysis because of the broad coverage of the economy.7
DSGE models are also used for forecasting, though there is no firm consensus among
practitioners about its effectiveness relative to other macroeconomic models. While the use ofBayesian estimation technique helps greatly in fitting DSGE models to the data, we also face theproblem of identification as the size of the model grows bigger.8
In practice, international organizations and resource-rich central banks are known to utilize
a “suite of models,” each tailored to different policy objectives. Thus, the question of whether todevelop and maintain an in-house DSGE policy model ultimately comes down to how much it
costs and whether the institution can afford it.9 Studies have reported that in order to build an inhouse DSGE policy model from scratch, it roughly takes three full-time employees over two
years. 10 Furthermore, division transfers, quits, and retirement pose additional challenges in
keeping the model operational at all times.11 However, DSGE policy models may have a cost7

Traditional macroeconometric models that are currently in use could range from the “classic” Cowles Commission type models(e.g., see Arnold, 2018) to more modern and computation-intensive types such as FRB/US (U.S. Board of Governors), LENS,IMPACT (Bank of Canada), and FR/BDF (Bank of France).
8 As such, institutions using large-scale DSGE models with a couple of hundreds of equations often use fully calibrated models.
For example, IMF uses the Global Integrated Monetary and Fiscal Model (“GIMF”), which contains thousands of model
equations/variables, and all model parameters are calibrated. In the parlance of the Bayesian method, applying calibration iseffectively equivalent to imposing a “degenerate” prior on the estimated parameters. On the historical origins and philosophy ofthe calibration method, see for example Dejong and Dave (2011).
9
Another factor is whether the management of a policy institution understands the usefulness of DSGE policy models and has theworking knowledge of applying it in the routine policy discussion.
10 Norges Bank spent 3 years with 3 full-time equivalent (FTE) workers, the Bank of England took 2 years and 3 months with 15
FTEs, and the Finnish Ministry of Finance took 5 years with 3FTEs (Hjelm et al., 2015; Saxegaard, 2017). Swiss National Bank
spent 2 years but with no disclosure on personnel involved (Cuche-Curti et al., 2009).
11
Hjelm et al. (2015) points out that a model use could face particularly severe challenges when its founder(s) leaves the institution.

6

advantage over traditional macroeconometric models. First, DSGE models are similar in its basic
structure and one can learn from other institutions that have already developed similar models.
Second, the existence of commonly used programming platforms (e.g., DYNARE) makes new
knowledge and structure relatively easy to be passed on within and across institutions.12 Lastly,
DSGE models are generally less costly in terms of computing power (i.e., numbers of memories,
cores, and servers required to run the program within a reasonable time frame) relative to otherlarge-scale macroeconometric models.13

3. Sample Description
3.1 Sample Selection Criterion
In selecting DSGE policy models, we mainly rely on manual online searches via RePEc(“Research Papers on Economics”) and Google Scholar.
First, we identified 16 “key” survey papers through multiple keyword searches (“macro,”“DSGE,” “Policy Model,” “Central Bank,” and “Ministry of Finance”). 14 We then used theirreferences to initiate our own forward/backward search process. Here, “forward” search is
conducted by checking the citation function in Google Scholar (“cited by”) or RePEc (tab“Citation”), whereas “backward” search is conducted by directly examining the main text andreference section of the model documentations. Once we spot a new documentation that is
seemingly related to a “macro” model (i.e., “DSGE,” “projection,” “traditional macroeconometric

12 Other coding platforms used in DSGE model development include IRIS (Czech National Bank, Finnish Ministry of Finance),
JULIA (Federal Reserve Bank of New York), MAPS (Bank of England), RISE/NB Toolbox (Norges Bank), YADA (ECB), andTROLL (IMF).
13 For computing power required to run state-of-the-art large-scale macroeconometric models, see Hirose (2020).
14
These papers are Blanchard (2018), Coenen et al. (2012), Fueki and Fukunaga (2011), Fukac and Pagan (2016), Hall et al. (2013),
Hjelm et al. (2015), Kilponen et al. (2019), Matsumae (2012, 2017), Murphy (2017), Okano (2017), Pagan (2003, 2019), Sergi(2017), Tovar (2009), Wieland et al. (2012).

7

model”) used by policy institutions, we record it and move to the next search. 15 However,overlapping generation models, VAR models, indicator models, dynamic factor models, andmicrosimulation models were precluded from our search.16 After the lengthy search and recording
process, we found 339 model documentations, which cover 196 macroeconomic models associatedwith 88 institutions.
Our next step is to examine the individual documentation and drop those that are related toeither traditional macroeconometric models or projection models. It should be noted that thisprocess is not as straightforward as one might think, since some of the modern non-DSGE macro
policy models feature modelling approaches used in a conventional DSGE model used in academia(e.g., rational expectation, steady-state, partial adjustment cost, Bayesian estimation). After thethorough examination, we identified 91 DSGE policy models. The earliest DSGE model
documentation in our sample was published in 2002.

3.2 Sample Classification
A key part of our classification is whether our sample model is owned by an institution orrather by an individual working for the institution. In the case of the latter, the model
documentation should be regarded as a personal research paper. In general, it is difficult to know
with certainty which of the two applies, thus we apply four criteria in judging the ownership.
The most important criterion for us is whether there is any “proper” documentation
available for the model. Such documentation may take the form of peer-reviewed journal
publications, institutional discussion papers, staff papers, technical notes, and slides with an

15 One reason for casting such a wide net is because sometimes the documentation itself is not clear in whether the model should
be categorized as a DSGE model or not. Also, documentation on non-DSGE models sometimes refers to DSGE models that
could not have been found otherwise.
16
Note, however, that DSGE models with overlapping generation structure is included in our sample.

8

institutional logo. The existence of such documentations serves as evidence that the managementof the institution, who we regard as the main client of the model, is at least aware of the existenceof the model. Of the 91 DSGE policy models that we identified initially, 84 accompanied at least
one documentation, but for the remaining seven we could not find a proper documentation.17 Thus
we treat this sample with 84 models as our baseline sample and use them in the analysis in Section
4.
The second criterion is whether a name (typically an acronym) accompanies the model. A
unique name indicates that the model development was authorized as an “internal” project by the
management of the institution.18 Among the 84 baseline sample models, 72 models have a name,but 12 did not.
The third criterion is whether the model is currently in use. This can be partially inferredfrom the name of the model: the model with a smaller number is (almost) always replaced by themodel with a larger number. In a few cases, the model is no longer in use based on the
documentation. Of the remaining 72 models, 19 models are either retired or replaced at the timeof writing.
Finally, when we conduct model comparison analysis in Section 5, we further restrict oursample to one model per institution to avoid overrepresenting models from large institutions thatown more than one DSGE model that are currently in use. In choosing the representative modelfor each institution, we pick the one with the most recent publication date. Based on these four

17 These models are developed by National Bank of Belgium (BE-3C), Deutsche Bundesbank (BBK model), European Central
Bank (NAGE), Spanish Ministry of Economy and Finance (EREMS2), Bank of France (French version of EAGLE), Bank of
Lithuania’s DSGE model, and International Monetary Fund (SIXMOD). We note that five of the seven institutions have developed
other DSGE models that are included in the baseline sample. Therefore, we argue that our sample is unbiased for the purpose of
our analysis.
18
Furthermore, if the name accompanies either a numerical or an alphabetical extension, that serves as a strong evidence that themodel is for institutional use.

9

criteria, our sample reduces to 42 models – which can be described as DSGE models that are
owned and actively used by 42 policy institutions.19

4. Descriptive Statistics
4.1 Distribution of institutions by the number of models per institution
In this section, we describe our baseline sample of models by the type of institutions, theaverage number of authors, and stated objectives.
In our sample, a total of 58 institutions are associated with at least one of the 84 DSGE
policy models.20 Table 2 shows the distribution of institutions based on the number of models per
institution. First, 34 of the sample institutions (58.6% of the total) possess only one DSGE policy
model, 16 institutions (27.6%) possess two models, and the remaining eight institutions possess
three or more models (13.8%). This implies that institutions that utilize a suite of DSGE models
remains a minority in our sample.21 Second, of the 58 institutions, nine engage in multi-regional(i.e. more than two regions) large-scale DSGE policy models, which we confirmed through reading
the documentations. Third, of the 58 institutions, ten engage in collaborative work with otherinstitutions, judging from the affiliation of the authors. The average number of models are 2.89 for
institutions that have multi-region models and 2.60 for institutions that collaborate with others.
Both numbers are notably higher than the overall average of 1.66. Finally, 30 of the 58 institutions
use non-DSGE models along with DSGE policy models.

4.2 Average number of authors

19

Samples chosen for this exercise are shown with asterisk in the Appendix Table A.1.
These institutions consist of 37 central banks, 16 government agencies, and 5 international organizations.
21
Appendix Table A.1 lists the institutions that have multiple DSGE policy models.
20

10In our sample, a total of 194 documentations are associated with the 84 models. The
average number of authors per documentation is 3.20, and roughly 60% of them are prepared bythree or more authors. To see how the average number of authors changed over time, Table 3 sorts
the model documentations by the year of publication (i.e. up to 2004, 2005-09, 2010-14, 2015-19).
We see that the average number of authors per documentation has been continuously rising (2.29,
2.65, 3.26, 3.65), along with the total documentation counts in our sample (7, 48, 74, 65). The
steady increase in the number of authors per documentation could be due to the increasing scale
and added complexity of the model. Overall, we conclude that developing DSGE policy models
involves team work and more so in recent years.

4.3 Stated Objectives
One possible reason that an institution owns multiple DSGE policy models is that somepolicy questions are best addressed using models tailored to a specific objective. To examine this,Figure 2 presents the frequency of stated objectives that appear in the model documentations.22 Of
the 58 institutions that we have examined, 48 refer to at least one objective, and 34 refer to multiple
objectives in the documentation. The top 3 most frequently stated objectives are policy analysis(44 cases), forecasting (20 cases), and interpreting observed business cycles fluctuations (12 cases).
We also examined the objectives by institutional type. We find that five central banks andthree international organizations mention understanding the nature of international economic
linkage as one of their primary objective. Six central banks explicitly note that they use DSGE
policy models to complement other macroeconomic models. And one central bank and twointernational organizations mention “risk analysis” as part of their objective, which is a relatively

22

Detailed descriptions of actual stated objectives for the individual models are presented in the Appendix Table A.2.

11

recent phenomenon. With regard to government agencies (e.g., Ministry of Finance), the statedobjectives (if any) are either policy analysis, forecasting, or interpreting business cycles, and noneof them mention understanding international linkages, complementing other models, or risk
analysis as their objectives.

4.4 Number of Models by Region and Institution TypeIn our baseline sample of 84 models, 72 of them are developed by central banks andgovernment agencies (53 institutions in total) and the remaining 12 models are developed byinternational organizations (five institutions in total). Table 4 tabulates the former type of models
based on geographical regions (Europe, America, Asia/Oceania, Middle East/Africa) andinstitution types (central banks, government agencies). First, we find that close to 80% of the
DSGE policy models are developed by central banks, which shows that central banks are the mainusers of the DSGE model.23 Second, we find that European government agencies have the largest
share among all government agencies (16 of the 17 models). The recent occurrence of the 2010
Euro Crisis could have contributed to the active development of DSGE models by European
institutions.
Figure 3 depicts the number of publications broken down by year and the type of
institution.24 We witness a steady increase of DSGE model development over the years, consistent
with the upward trend in the number of sample documentations seen earlier (Table 3). Part of theincrease also reflects updating the existing model by incorporating the credit channel in the model
developed in the pre-Global Financial Crisis era (up to 2007). However, the main push comes fromthe increased involvement of (mostly European) government agencies, which has increased their

23
24

For non-DSGE macroeconomic models (103 models in our sample), central banks’ share reduces to around 50%.
The list of main reference is shown in Appendix Table A.3.

12

share across institution types from 10% to 24% since 2010. Many of the models developed by
European government agencies simulate the effect of structural reforms augmented with rich fiscal
policy options, which is likely to be motivated by the occurrence of the recent Euro Crisis.

5. Model Comparison
In this section, we provide model comparison based on five important model parts andparameters for the 42 models that are owned and actively used by policy institutions. In fact, the42 models have many common model parts. Their basic structure follows the medium-scale
DSGE model a la Smets and Wouters (2007), augmented with the small open economy
assumption. Fiscal policy generally follows the tax rate rule with debt stabilization component.
The total number of equations typically exceeds one hundred. The flow of technicaldocumentations also follows similar patterns: it typically starts with the derivation of theindividual model equations, then goes on to parametrization strategy, and ends with simulation
results for different hypothetical scenarios using forecast error variance decomposition and/or
impulse response functions.
As it turns out, much of the differences across the models occur in the treatment of thehousehold sector. Therefore, we focus on five household characteristics: a) intertemporal elasticity
of substitution for consumption (“IES”), b) habit, c) household heterogeneity, d) financial friction,and e) labor supply.25 The difference across models can occur in terms of the choice of model parts(e.g., the type of financial frictions) and in how to parameterize the model.

5.1 Intertemporal Elasticity of Substitution (IES)25

We regard financial friction as part of household characteristics because households are the net saver in the economy which isultimately responsible in providing loanable funds to the corporate sector (= net borrower).

13

One of the main features of DSGE models is the New Keynesian IS curve, which is derived
by solving an intertemporal optimization problem for households’ consumption. The model
equation implies that consumption growth is a function of the short-term interest rate. The strengthof the consumption response is represented by the IES parameter. In the academic DSGE literature,
IES is usually treated as a time-invariant parameter and is often assigned a value of one, becausesuch a parameterization will generate model predictions that are consistent with the stylized factsof long run economic growth. In empirical studies, however, this parameter is generally found to
be much smaller than one, and sometimes even close to zero. Since the size of the IES is directly
related to how strongly economic variables (particularly consumption) respond to exogenous
shocks, assigning a large parameter value may lead to overstating the effect of policy intervention.
Figure 4(a) shows the distribution of the consumption IES parameter. 36 out of the 42
models adopt the calibration method and the remaining six use the Bayesian estimation method.
Among the 36 calibrated cases, 28 adopted a value of one.26 Figure 4(b) shows the distribution of
IES for the 14 models that adopted a nonunitary value (eight are calibrated and six are estimated),
and the largest mass occurs between 0.3 and 0.7. This is consistent with Havranek et al. (2015),
which reports a mean estimate of 0.5 using the 169 academic studies in their meta-study. However,
the majority of the DSGE policy models that adopted a value of one seems to be contradictory towhat the recent literature has found.27

5.2 Habit

26
More precisely, 20 models adopt the log utility, which implies IES of one under CRRA utility. The remaining eight set the IES
parameter to be one at the parameterization stage.
27
We further confirm that the year of the publication has nothing to do with what values of IES is adopted. The average
publication year of the main reference documentation that set IES to one is 2013.9, whereas the same average for those that adopt
non-unitary IES is 2013.4.

14During the 1990s and early 2000s, one of the major criticisms towards DSGE models wasthat DSGE models were unable to replicate the “hump-shaped” responses of economic variables
in response to economic shocks. To address this problem, habit formation in consumption was
introduced in academic DSGE models and gradually became the norm in the early 2000’s.
Formally, habit formation assumes that households’ utility depends on consumption exceeding thepast reference level, either in external or internal form.28 By choosing a positive value on the habit
parameter, households’ utility function becomes time-inseparable and history dependent. As aresult, households would readjust their consumption level more gradually in response to shocks,
thereby generating a seemingly realistic hump-shaped consumption response.
In our sample, 36 out of 42 models apply habit formation in consumption, of which 22 areexternal and 14 are internal. The remaining eight models do not incorporate any form of habit
formation. Figure 5 shows the distribution of the consumption habit parameter across the 36
models. The overall sample mean is 0.67, and the largest mass occurs between 0.6 and 0.8. The
mean of the calibrated habit parameter is 0.65, not much different from the mean of 0.71 for theestimated ones.
We further check whether the type of habit makes any difference. The mean estimate of
the external habit parameter is 0.66, not much different from that for the internal habit (0.70).
When the parameter is calibrated, the average values for both habit types are almost identical (0.71
for the external habit and 0.72 for the internal habit). When the parameter is estimated, the gap
widens somewhat (0.62 for the external habit and 0.68 for the internal habit). In conclusion, the28External habit applies when the consumption level of a household is compared against the overall consumption (usually
lagged), which captures the effect that is often referred to as “Keeping up with the Joneses” effect. Internal habit applies when the
consumption is compared across the same household at different timing. Also, note that while we focus on habit formation thatapplies to consumption in this paper, the concept itself can be introduced for other variables such as durables (e.g., housing),
financial assets, and labor supply.

15

distinction between external and internal habit seems inconsequential in the choice of parametervalues.
We shall note that the values adopted for the habit parameter are somewhat higher than theestimates from empirical studies. According to the meta-study by Havranek et al. (2017), the mean
estimate of 81 studies published in academic journals is 0.43 (0.57 when aggregate data is used
and 0.10 when micro-based data is used). Again, the inconsistency between DSGE policy models
and empirical studies could potentially mislead policymakers in interpreting the effect of policy
intervention on consumption dynamics both in terms of magnitude and persistence.

5.3 Heterogeneity of Households
There is a longstanding debate in macroeconomics about what proportion of households
behaves rationally in a forward-looking manner (“Ricardian”). In the DSGE model setting,Ricardian households would reduce consumption in response to an expansionary fiscal shock, sothat they can prepare for any future tax increase. This behavior contradicts the existing VAR results(e.g., Blanchard and Perotti, 2002) in which consumption responds positively in response to an
expansionary fiscal policy.29
There are a few solutions offered by the academic literature. One is to introduce “hand-tomouth (HtoM)” households, who are forced to consume all their income in the current period (Galiet al, 2007). By setting a sufficiently large share of HtoM households (0.25 according to Gali etal., 2007), the overall consumption would respond positively to an expansionary fiscal shock.
Alternatively, one could also introduce heterogeneity in how people discount future utility. Thiscan be done either by assuming a stochastic rate of death (perpetual youth model, e.g., Blanchard,

29
Ramey (2019) notes that the bulk of the estimates for the multiplier on general government purchases on GDP lies in the rangeof 0.6 to 1.

16

1985) or a heavier future discount for borrowers relative to savers (Kiyotaki and Moore, 1997;
Iacoviello, 2005). Both approaches make households behave myopic relative to the Ricardian
households.
In our sample, exactly half (21 models) introduce some forms of heterogeneity amonghouseholds. The breakdown is overwhelmingly in favor of the HtoM approach (19 models),followed by the perpetual youth model (3 models) and the approach of a heavier discount forborrowers (2 models). 30 Government agencies have a higher chance of incorporating
heterogeneous households in their model (88%, 7 out of 8 models), relative to central banks (40%,
12 out of 30) and international organizations (50%, 2 out of 4). This is likely because consumption
response to fiscal stimulus is of primary interest to government institutions.
With regard to parameterization of household heterogeneity, most institutions (86%, 18 outof 21) apply the calibration method. Figure 6 shows the distribution of the fraction of the HtoM
households for the relevant 19 models. The largest mass is between 0.3 and 0.4. The calibrated
values are mainly concentrated in the upper (= right) tail, whereas the estimated values areconcentrated in the lower (= left) tail. More than 80% of the calibrated values exceed the thresholdvalue of 0.25 as noted by Gali et al. (2007). This is counter to the generally lower values found in
the empirical studies.31 In general, one should be cautious of setting a value too high for thisparameter because it could inflate the fiscal multiplier and strengthen the policy effect within themodel.

5.4 Financial Friction30
31

Three models use combination of the two approaches.
See for example, Hara et al. (2016), Havranek and Sokolova (2020), and Slacalek et al. (2020).

17

In traditional DSGE models, households provide funds directly to firms without any
friction. This modelling strategy seemed no longer adequate after the 2008 Global Financial Crisis,
during which financial intermediaries played a key role. In recent years, academics have come up
with various ways to introduce financial frictions to DSGE models.32
In our sample, only 15 of the 42 models incorporated financial frictions of some sort. Thisratio appears to be low given the importance of financial frictions emphasized by the academic
communities and lively policy discussions in the post-Global Financial Crisis era. When broken
down by institution type, a little over one-third of the central banks incorporated financial frictionin their model (37%, 11 out of 30 models), which is somewhat higher than government agencies(25%, 2 out of 8). This finding is not surprising because the credit channel of policy transmission
is perceived to be more relevant to the monetary policymaker than the fiscal policymaker.33 With
regard to the type of financial friction, the results are highly mixed: seven of the 15 institutions
adopted the financial accelerator mechanism at the nonfinancial firm level (cf. Bernanke et al.,1999), four adopted an explicit banking sector (cf. Atta-Mensah and Dib, 2008; Gerali et al., 2010;
Gertler and Karadi, 2011), three adopted a collateral constraint (cf. Kiyotaki and Moore, 1997;
Iacoviello, 2005), and two incorporated liquidity needs for firms (cf. Christiano and Eichenbaum,
1995; Christiano et al., 2008).34 The existence of different approaches may partially explain why
many of the institutions remain on the fence with regard to incorporating financial friction into
their model.35

32 See Christiano et al. (2018) for a comprehensive overview on this topic.
33
In graduate-level textbooks, the term “credit channel” is often automatically regarded as a policy transmission channel exclusive
to monetary policy. However, Yagihashi (2020) shows that the cost of credit channel misspecification in the DSGE model could
be potentially larger for the fiscal policymaker relative to the monetary policymaker.
34
One model incorporates both an Iacoviello-type financial accelerator mechanism and a Gerali et al.-type banking sector.
35
Yagihashi (2018) demonstrates that the “wrong” pick of financial frictions can lead monetary policymakers to choose asuboptimal monetary policy through their misspecified model.

185.5 Labor Supply
One of the long-standing “puzzles” in the macroeconomic literature is that the estimate for
the Frisch labor supply elasticity tends to be high when using macro data and low when using
micro data.36 In our sample, 38 out of the 42 models present the Frisch elasticity: 23 are calibrated
and 15 are estimated using macro time series data. 37 Figure 7 shows the distribution of theparameter value for the 38 models both for calibrated and estimated cases. Calibrated parametersthat generally use existing micro studies as their reference tend to have lower values than estimatedparameters. This is consistent with the literature that generally finds larger values when using
aggregate data compared with micro data.
This result also illustrates policymakers’ dilemma of whether to adopt a micro-based andempirically more relevant Frisch elasticity or to estimate the parameter with the Bayesian
technique that improves model fit (Section 2.3). Such a dilemma could be mitigated by introducing
unemployment (= extensive margin of labor supply) into the model. Introducing unemployment
can generate a large swing in aggregate hours without resorting to the implausibly large Frischelasticity. In academic circles, two approaches have become popular in introducing unemployment:
one is search and matching frictions as in Mortensen and Pissarides (1994) and the other is a utility
threshold approach as in Gali et al. (2012).
In our sample, seven of the 38 models introduced unemployment, four of which adopted
search and matching frictions and the remaining three adopted the utility threshold approach. The
average Frisch elasticity when unemployment is introduced is slightly lower than the overall

36 Chetty et al. (2013) conduct a meta-study on the micro-based labor supply elasticity estimates and conclude that the Frischelasticity is likely to be less than one.
37
Of the four remaining models, two do not explicitly model labor supply and two use the leisure-in-the-utility function approachbut do not provide parameter values that allow us to calculate the implicit labor supply elasticity.

19

sample (0.65 as opposed to 0.82).38 This indicates that introducing unemployment in the model
can bring the macro and micro-based labor supply elasticities closer to each other withoutcompromising model fit. But we should note that the remaining 31 models solely consider theintensive margin of labor supply.

5.6 A Score-card Approach for DSGE Policy Models
In the final segment, we attempt to evaluate each policy model with respect to the following
five criteria that we have examined so far:
1. Does the model adopt nonunitary value for the consumption IES?
2. Does the model allow habit formation in consumption?
3. Does the model introduce heterogeneity in households?
4. Does the model incorporate financial frictions?
5. Does the model include unemployment?
For the sake of simplicity, the model scores one point for each “yes” to the above questions, thatis, the maximum score is a five.39
Figure 8 shows the distribution of scores for our baseline sample of 42 models. No
institution receives the maximum score of five and only two models score four points.40 While
there is no model that scores a zero, as much as nine models score a meager one point (21.4% of
the total). The overall average score is 2.21. This seemingly low average score indicates that the

38 Chetty et al. (2013) report that in their sample the unweighted mean for the extensive-margin Frisch elasticity is 0.32.
Combined with the intensive-margin Frisch elasticity ranging from 0.37 to 0.7, the overall Frisch elasticity of aggregate hours
would add up to 0.69-1.02.
39
Note that we are not trying to argue that the stated five criteria are equally important to all DSGE models because the institution
may have different objectives and emphasis. We simply evaluate whether the DSGE policy models incorporated recent academic
findings.
40
The two highest scoring models are BoC-GEM-Fin developed by Bank of Canada and FiMod developed jointly by Banco deEspana and Deutsche Bundesbank.

20

five criteria may be controversial within the policy community. Furthermore, there is no clear
winner in terms of institution type: the average scores of central banks, government agencies, andinternational organizations are 2.23, 2.13, and 2.25, respectively.
One possible explanation is that only resource-rich institutions can afford a DSGE modelthat meet all of our criteria. Using the number of DSGE models owned by an institution as a proxyfor resource-rich institutions, we find that the correlation coefficient between the number of DSGE
models of a given institution and the score of its representative DSGE model is 0.327, which shows
mild positive association. Another possible explanation is that newer models have an advantage
over older models in terms of incorporating new modeling techniques. The correlation coefficient
between the year of publication of the main reference documentation and the score is 0.169. Whilethe hypothesis of the late-mover advantage is confirmed, the association seems much lower than
one would anticipate.
Table 5 reports the correlation coefficients between a given pair of evaluation criteria,
which represents how likely the two model parts are chosen together. We find positive correlation
coefficients in three out of the possible ten cases, indicating that the relevant model parts tend to
be chosen together. These are non-unitary IES and heterogeneous households (corr. coef. = 0.20),
habit formation and unemployment (0.18), and heterogeneous households and unemployment(0.06). There are two interpretations for the observed positive associations. First, it may simply
reflect the sequence of model updates, which parallels model development in the academic DSGE
literature. Second, the positive signs show that the model parts are complementary in nature, i.e.,need both of them to yield the anticipated model outcome.

7. Conclusions

21

This paper surveys recent DSGE models used by public institutions around the worldusing publicly available documentations. We show that over the years there is a steady increasein the development of DSGE models by policy institutions, particularly among Europeans andafter the 2008 Global Financial crisis. Many institutions have multiple DSGE models serving
different purposes. Over time, DSGE models have also become more complex and involve more
researchers in model development.
Although the use of DSGE models among policy institutions has been growing,
consensus has been hardly reached on modelling practice. We demonstrate this by comparing
model features along five dimensions using 42 models from policy institutions. We find large
variations in both modelling strategies and adopted parameter values, some of which do notreflect the findings of the recent empirical literature. We conclude that there is a great need for
future DSGE policy models to adopt more recent academic findings in order to continue serving
as a credible tool for policymaking.

Reference
Arnold, R. (2018). “How CBO Produces Its 10-Year Economic Forecast,” Congressional Budget
Office Working Paper 2018-02, 1-25, Congressional Budget Office, February 2018.
Atta-Mensah, J. and A. Dib (2008). “Bank Lending, Credit Shocks, and the Transmission of
Canadian Monetary Policy,” International Review of Economics & Finance, 17(1), 159–
176.
Bernanke, B. S., M. Gertler, and S. Gilchrist (1999). “The Financial Accelerator in a Quantitative
Business Cycle Framework,” Handbook of Macroeconomics, 1, 1341–1393.
Blanchard, O. J. (1985). “Debt, Deficits, and Finite Horizons,” Journal of Political Economy,
93(2), 223–247.
Blanchard, O. J. (2018). “On the Future of Macroeconomic Models,” Oxford Review of
Economic Policy, 34(1–2), 43–54.
Blanchard, O. J. and R. Perotti (2002). “An Empirical Characterization of the Dynamic Effects
of Changes in Government Spending and Taxes on Output,” The Quarterly Journal of
Economics, 117(4), 1329–1368.
Burgess, S., E. Fernandez-Corugedo, C. Groth, R. Harrison, F. Monti, K. Theodoridis, and M.
Waldron (2013). “The Bank of England’s Forecasting Platform: COMPASS, MAPS, EASE
22

and the Suite of Models,” Bank of England Working Paper No.471, 1-107, Bank ofEngland, May 2013.
Chetty, R., A. Guren, D. Manoli, and A. Weber (2013). “Does Indivisible Labor Explain the
Difference Between Micro and Macro Elasticities? A Meta-analysis of Extensive Margin
Elasticities,” NBER Macroeconomics Annual, 27(1), 1-56.
Christiano, L. J. and M. S. Eichenbaum (1995). “Liquidity Effects, Monetary Policy, and theBusiness Cycle,” Journal of Money, Credit and Banking, 27(4), 1113-1136.
Christiano, L. J., M. S. Eichenbaum, and C. L. Evans (2005). “Nominal Rigidities and the
Dynamic Effects of a Shock to Monetary Policy,” Journal of Political Economy, 113(1), 1–
45.
Christiano, L. J., M. S. Eichenbaum, and M. Trabandt (2018). “On DSGE Models,”
Journal of Economic Perspectives, 32(3), 113-140.
Christiano, L. J., R. Motto, M. Rostagno (2008). “Shocks, Structures or Monetary Policies?
The Euro Area and US After 2001,” Journal of Economic Dynamics and Control, 32(8),2476-2506.
Coenen, G., C. J. Erceg, C. Freedman, D. Furceri, M. Kumhof, R. Lalonde, D. Laxton, J. Lindé,
A. Mourougane, and D. Muir (2012). “Effects of Fiscal Stimulus in Structural Models,”American Economic Journal: Macroeconomics, 4(1), 22–68.
Cogley, T. and T. Yagihashi (2010). “Are DSGE Approximating Models Invariant to Shifts in
Policy?” The B.E. Journal of Macroeconomics, Vol.10(1) (Contributions), Article 27.
Cuche-Curti, N. A., H. Dellas, and J. Natal (2009). “DSGE-CH: A Dynamic Stochastic General
Equilibrium Model for Switzerland,” Swiss National Bank Economic Studies, No.5, 2009.
DeJong, D. N. and C. Dave (2011). Structural Macroeconometrics. Princeton University
Press.
Fueki, T. and I. Fukunaga (2011). “Medium-Scale Japanese Economic Model (M-JEM):
Chuukibo Dougakuteki Ippan Kinkou Moderu no Kaihatsu Joukyou to Katsuyourei,” (inJapanese) Bank of Japan working paper series, No.11-J-8, Bank of Japan, June 2011.
Fukac, M. and A. Pagan (2016). “Structural Macroeconometric Modeling in a Policy
Environment,” In Handbook of Empirical Economics and Finance, 234–265, Chapman andHall/CRC, 2016.
Galí, J., D. López-Salido, and J. Vallés (2007). “Understanding the Effects of Government
Spending on Consumption,” Journal of the European Economic Association, 5(1), 227–270.
Galí, J., F. Smets, and R. Wouters (2012). “Unemployment in an Estimated New KeynesianModel,” NBER Macroeconomics Annual, 26(1), 329–360.
Gerali, A., S. Neri, L. Sessa, and F. M. Signoretti (2010). “Credit and Banking in a DSGE Modelof the Euro Area,” Journal of Money, Credit and Banking, 42, 107–141.
Gertler, M., and P. Karadi (2011). “A Model of Unconventional Monetary Policy,” Journal of
Monetary Economics, 58(1), 17–34.
Hall, T., J. Jacobs, and A. Pagan (2013). “Macro-Econometric System Modelling @75,” Centre
for Applied Macroeconomic Analysis Working Paper 50/2019, 1-42, Crawford School of
Public Policy, The Australian National University, July 2013.
Hara, R., T. Unayama, and J. Weidner (2016). “The Wealthy Hand to Mouth in Japan,”
Economics Letters, 141, 52–54.
Havranek, T., R. Horvath, Z. Irsova, and M. Rusnak (2015). “Cross-Country Heterogeneity inIntertemporal Substitution,” Journal of International Economics, 96(1), 100–118.

23

Havranek, T., M. Rusnak, and A. Sokolova (2017). “Habit Formation in Consumption: A MetaAnalysis,” European Economic Review, 95, 142-167.
Havranek, T., A. Sokolova (2020). “Do Consumers Really Follow a Rule of Thumb? ThreeThousand Estimates from 144 Studies Say “Probably Not”,” Review of Economic
Dynamics, 35, 97-122.
Hjelm, G., H. Bornevall, P. Fromlet, J. Nilsson, P. Stockhammar, and M. Wiberg (2015).
“Appropriate Macroeconomic Model Support for the Ministry of Finance and the National
Institute of Economic Research: A Pilot Study,” National Institute of Economic ResearchWorking Paper, No.137, 1-61, National Institute of Economic Research, March 2015.
Iacoviello, M. (2005). “House Prices, Borrowing Constraints, and Monetary Policy in theBusiness Cycle,” American Economic Review, 95(3), 739–764.
Kilponen, J., M. Pisani, S. Schmidt, V. Corbo, T. Hledik, J. Hollmayr, S. Hurtado, P. Júlio, D.
Kulikov, and M. Lemoine (2019). “Comparing Fiscal Consolidation Multipliers AcrossModels in Europe,” International Journal of Central Banking, 15(3), 285–320.
Kiyotaki, N. and J. Moore (1997). “Credit Cycles,” Journal of Political Economy, 105(2), 211248.
Kydland, F. E. and E. C. Prescott (1982). “Time to Build and Aggregate Fluctuations,”
Econometrica, 50(6), 1345–1370.
Lucas, R. E. (1976). “Econometric Policy Evaluation: A Critique,” In Carnegie-Rochester
Conference Series on Public Policy, 1, 19–46.
Matsumae, T. (2012). “DSGE Moderu no Haikei, Gaiyou, Ouyourei,” (in Japanese) mimeo, 1-34.
Matsumae, T. (2017). “Essays on the Empirical DSGE Approach: Estimation Methodologies andApplications,” Ph.D. Thesis, 1-254, Tohoku University, August 2017.
Mortensen, D. and C. Pissarides (1994). “Job Creation and Job Destruction in the Theory of
Unemployment,” Review of Economic Studies, 61, 397-415.
Murphy, C. (2017). “Review of Economic Modelling at The Treasury,” Report Prepared for the
Department of the Treasury, 1-96, Independent Economics, March 2017.
Okano, M. (2017). “Dougakuteki Kakuritsuteki Ippan Kinkou Moderu no Kaihatsu Oyobi
Katsuyou ni Tsuite no Ichikousatsu,” (in Japanese) Keizaigaku Ronkyuu, 71(2), 119–140.
Pagan, A. (2003). “Report on Modelling and Forecasting at the Bank of England/Bank’s
Response to the Pagan Report,” Bank of England Quarterly Bulletin, 43(1), 60-88.
Pagan, A. (2019). “Australian Macro-Econometric Models and Their Construction-A ShortHistory,” Centre for Applied Macroeconomic Analysis Working Paper, 50/2019, 1-41,
Crawford School of Public Policy, The Australian National University, July 2019.
Ramey, V.A. (2019). “Ten Years After the Financial Crisis: What Have We Learned from theRenaissance in Fiscal Research,” Journal of Economic Perspectives, 33(2), 89-114.
Saxegaard, M. (2017). “The Use of Models in Finance Ministries–An Overview,” Arbeidsnotat,
2017/1, 1-33, Norwegian Department of Finance, February 2017.
Sergi, F. (2017). “The Standard Narrative on History of Macroeconomics: Central Banks andDSGE Models,” Paper Prepared for the Annual Meeting of the History of Economic
Society, Toronto, June 2017.
Slacalek, J., O. Tristani, and G. L. Violante (2020). “Household Balance Sheet Channels ofMonetary Policy: A Back of the Envelope Calculation for the Euro Area,” NBER Working
Paper Series, Working Paper 26630, National Bureau of Economic Research, Cambridge,MA.

24

Smets, F. and R. Wouters (2007). “Shocks and Frictions in US Business Cycles: A Bayesian
DSGE Approach,” American Economic Review, 97(3), 586–606.
Tovar, C. E. (2009). “DSGE Models and Central Banks,” Economics: The Open-Access, OpenAssessment E-Journal, 3, 1-31.
Wieland, V., T. Cwik, G. J. Müller, S. Schmidt, and M. Wolters (2012). “A new
Comparative Approach to Macroeconomic Modeling and Policy analysis,” Journal of
Economic Behavior & Organization, 83(3), 523-541.
Yagihashi, T. (2018). “How Costly is a Misspecified Credit Channel DSGE Model in
Monetary Policymaking?” Economic Modelling, 68, 484-505.
Yagihashi, T. (2020). “The Cost of Omitting the Credit Channel in DSGE Models: A
Policy Mix Approach,” PRI Discussion Paper Series, 20A-05, Ministry of Finance,
Japan, March 2020.

25

Appendix
Table A.1: Institutional Rankings Based on the Number of DSGE Policy Models They Own# ofModels5
4

32Institution (Country)

Banca d’ Italia (ITA)
European Central BankInternational Monetary Fund
The Bank of Finland (FIN)
Federal Reserve Bank of NY (USA)
Banco de Espana (ESP)
Ministry of Economy & Finance (ITA)
Banco de Portugal (POR)
Bank of Canada (CAN)
Deutsche Bundesbank (DEU)
Spanish Ministry of Economy and Finance (ESP)
Reserve Bank of New Zealand (NZL)
Sveriges Riksbank (SWE)
Federal Reserve Board (USA)European Commission
Central Bank of Chile (CHL)
SEPG (ESP)
Ministry of Finance and Public Administration (ESP)SEEAE (ESP)
Luxembourg Ministry of Economy and Trade (LUX)
Central Bank of Malta (MLT)
National Bank of Poland (POL)
National Bank of Slovakia (SVK)

Use nonDSGE
Models?
yes
yes
yes
no
no
yes
yes
no
yes
yes
yes
yes
yes
yes
no
no
no
no
no
no
no
no
no

Note: The rankings are based on the baseline sample of 84 models. To save space, we only list
institutions that have two or more DSGE models. Non-DSGE macro models refer to either traditional
macro models or projection models, which are confirmed via their documentations.

26

Table A.2: Stated Objective for DSGE Policy Models
Country/Institution

Statement

AUS

Reserve Bank
of Australia

BRA

Banco Central
do Brazil

CAN

Bank of
CanadaCHE

Swiss National
Bank

CHL

Central Bankof Chile

COL

Bank of the
Republic(Columbia)
Czech
National Bank
Ministry of
Finance of the
Czech
Republic“The model is part of a set of macroeconomic models maintained by the
Economic Research Department at the RBA. These models complement,
but do not substitute for, the more detailed sectoral analysis and judgementbased projections.”; “The model can be used to provide scenario andsensitivity analysis. It also provides a crosscheck on forecasts produced by
reduced-form econometric techniques and judgement.” (Rees et al., 2016)“The current BCB macroeconomic modeling framework comprises a suiteof models, including small and medium-size semi-structural models, vector
autoregression (VAR) models and auxiliary structures that are used toanswer specific policy issues. The DSGE model is an important additional
tool to that framework.” (de Castro et al., 2015)“Bank staff have used the BoC-GEM to analyze various issues and to model
how those issues could affect Canada either directly or indirectly.” (Bailliu
et al., 2010); “We use BoC-GEM-FIN to study the effects ofcountercyclical bank capital requirements on macroeconomic stability in theU.S.” (deResende et al., 2016)“The model is expected to serve as a laboratory for a) studying businesscycles in Switzerland, b) examining the effects of actual and hypotheticalmonetary policies, and c) projecting (forecasting) the likely course of events– under various scenarios – for the Swiss economy in the short to medium
term.” (Cuche-Curti et al., 2009)“The Central Bank of Chile has been using dynamic stochastic generalequilibrium (DSGE) models for regular policy analysis and medium-term
projections for its Monetary Policy Report since the late 2000s” (Garcia etal., 2019)“PATACON was designed to be useful for analyzing Colombian
macroeconomic data and to help guide monetary policy discussion.”(Gonzalez et al., 2012)“The new structural model (g3) has been used as the core forecasting tool
since July 2008.” (Andrle et al., 2009)“The Ministry of Finance of the Czech Republic has developed an extended
version of the DSGE model. It serves various purposes. It primarily
supports macroeconomic forecasts by evaluating model scenarios on aquarterly basis. Moreover, the model is employed for simulation purposes
related to changes in fiscal policy parameters, and also for assessing thesensitivity of macroeconomic variables to various shocks to the economy.”(Aliyev et al., 2014)“We use the model to assess how discretionary fiscal policy in Germany
and the Euro Area affected GDP growth during the crisis, evaluate
spillovers of fiscal policy and calculate various present-value multipliers for
distinct fiscal instruments.” (Gadatsch et al., 2016)“The model has been used for policy simulations in the Working Group on
Econometric Modelling (WGEM) of the European System of Central Banks(ESCB).” (Staehler and Thomas, 2012)

CZE
CZE

DEU

DeutscheBundesbank

ESP /
DEU

Banco deEspana /DeutscheBundesbank

27

ESP

Banco deEspanaESP

SpanishMinistry of
Economy and
Finance
Economic
Office of the
President of
Spain
Bank of
Estonia

ESP

EST

EU

EuropeanCentral Bank

EU /
PRT /
ITA

EuropeanCentral Bank /Bank of
Portugal /Bank of Italy

EU

EuropeanCommission

EU

European
Stability
Mechanism
Bank of
Finland

FIN“In particular, a version of this model is expected to be used in the near
future to run simulations and alternative scenario analysis for the Spanish
economy within the forecast process. Therefore, one of the requirements ofthe model is to replicate the developments of most of the variables includedin the Spanish Quarterly National accounts, which is why the model
structure is more elaborated than what is standard in the literature.” (Andres
et al., 2006)“In the last eight years, the REMS model has become one of the reference
tools used by different institutions for ex-ante macro evaluation of theeffects of a number of policies and shocks affecting the Spanish economy.”(Bosca and Ferri, 2016)“MEDEA is a dynamic stochastic general equilibrium (DSGE) model thataims to describe the main features of the Spanish economy for policy
analysis, counterfactual exercises, and forecasting.”“One of the main goals of building a DSGE model for Estonia is to use it tounderstand the monetary policy and export-import linkages between a small
open economy of Estonia and the much bigger euro area economy. The listof other potential uses of the new model includes simulation exercises,
policy advice and forecasting of the main macroeconomic aggregates.”(Gelain and Kulikov, 2009)“…aim pursued in the development of NAWM2… to provide a structural
framework useable for assessing the macroeconomic impact of the ECB’slarge-scale asset purchases…” (Coenen et al., 2018)“The model (EAGLE, Euro Area and Global Economy model) is
microfounded and designed for conducting quantitative policy analysis ofmacroeconomic interdependence across regions belonging to the euro areaand between euro area regions and the world economy.” (Gomes et al.,2012); “Our results aim at explaining the domestic and cross-country
transmission mechanism of various shocks in a monetary union model
where financial factors do matter.”, “…EAGLE-FLI allows us to conduct aquantitative analysis in a theoretically coherent and fully consistent model
setup, clearly spelling out all the policy implications.” (Bokan et al., 2018)“Specifically, the GM model has been developed for three main purposes,
namely (1) the structural interpretation of business cycle dynamics, (2)contributions to the European Commission’s economic forecast, and (3)scenario analysis and policy counterfactuals” (Albonico et al., 2019);“QUEST is the global macroeconomic model that the Directorate Generalfor Economic and Financial Affairs (DG ECFIN) uses for macroeconomic
policy analysis and research.”, “Model variants have been estimated using
Bayesian methods, jointly with colleagues at the Commission's Joint
Research Centre (JRC). These dynamic stochastic general equilibrium(DSGE) models are used for shock analyses and shock decompositions, forexample, to assess the main drivers of growth and imbalances.” (retrieved
from the EC website on May 15, 2020)“We present EIRE Mod, a quarterly DSGE model developed for
macroeconomic policy analysis in Ireland.” (Clancy and Merola, 2016)“Since the fall of 2015, Aino2.0 has been used as the main forecastingmodel of the Bank of Finland.”; “To be clear, Aino 2.0 is by no means the

28

FIN

GBR

GRC
HUN

FinnishMinistry of
FinanceBank ofEngland

Bank of
GreeceCentral Bankof Hungary

IMF

International
Monetary
Fund

ISL
ISR

Central Bankof Iceland
Bank of Israel

ITA

Bank of Italy

ITA

Department of
the Treasury,
Ministry of
Economy and
Finance, Italy

IRL

ESRI/Ministryof Finance,
Ireland
Ministry of the
Economy andForeign Trade

LUX

only input into the forecasting and policy processes at the Bank of Finland;
several other models are also used.” (Kilponen et al., 2016)“In-house was chosen? Why? 1) Need to increase human capital 2)
Commitment 3) Continuity.” (Kuismanen, 2016)“COMPASS is intended to serve three key purposes: to be the mainorganizing framework for the construction of the forecast; to analyse andexplain the forecast (projection analysis); and to construct experiments toassess the sensitivity of the forecast to alternative assumptions (scenario
analysis).” (Burgess et al., 2013)“… developed at the Bank of Greece as a quantitative tool for policy
analysis.” (Papageorgiou, 2014)“PUSKAS was used in monetary policy decision support to produce
historical shock decompositions, enabled the carrying out of welfare
analysis and was able to perform counterfactuals without exceedingly
abusing the Lucas Critique.” (Szilagyi et al., 2013)“The Global Integrated Monetary and Fiscal Model (GIMF) is a multiregion, forward-looking, DSGE model developed by the Economic
Modeling Division of the IMF for policy analysis and international
economic research.” (Anderson et al., 2013); “… used by the IMF for avariety of tasks including policy analysis, risk analysis, and surveillance.”(Kumhof et al., 2010); “MAPMOD has been designed specifically to study
vulnerabilities associated with excessive credit expansions and asset pricebubbles, and the consequences of different macroprudential policies that
attempt to guard against or cope with such vulnerabilities.” (Benes et al.,
2014)
“The model has been developed at the Central Bank of Iceland as a tool in
support of inflation targeting.” (Seneca, 2010)“a. To provide a basis for discussion”; “b. Now casting and forecasting”; c.
To evaluate alternative policy measures and economic scenarios.” (Argov etal., 2012)“… evaluates the macroeconomic effects of the corporate sector purchaseprogramme (CSPP) implemented in the euro area by the Eurosystem.”(Bartocci et al., 2017)“Notably, IGEM has been designed to study the impact and the propagation
mechanism of temporary shocks, evaluate the impact of alternative
structural reform scenarios and analyze the effects of single policy
interventions and fiscal consolidation packages in Italy.” (Annicchiarico etal., 2016); “With this new variant of IGEM we are able to answer thefollowing economic policy questions. Which are the macroeconomic effectsof the rationalization of public spending? Which are the implications ofmajor advances in the implementation of the digital agenda? How do thesimplification reforms impact on the economy when the PA sector is
explicitly modeled? What happens if the overall productivity of the public
sector increases?” (Annicchiarico et al., 2017)“The primary aim of FIR-GEM is to serve as a fiscal policy toolkit for fiscal
policy analysis in Ireland.” (Varthalitis, 2019)“The resulting model is then calibrated to match the specific characteristicsof the Luxembourg economy, and used to assess the consequences of aseries of policies targeting the financial sectors.”; “This is the distinctive

29

MLT

Central Bankof Malta

NOR

Norges Bank

NZLReserve Bankof New
ZealandCentralReserve Bankof Peru

PER

PHL
POL

Central Bankof Philippines
National Bankof Poland

PRT

Banco dePortugal

SRB

National Bankof Serbia

SVK

National Bankof Slovakia

feature of LSM2, which makes it more suitable than the other models forpolicy simulations, though perhaps less adapted to other uses, such as shortand medium-term forecasting.” (Deak et al., 2012)“… this paper presents a unifying framework to quantify the impact ofstructural reforms aimed at increasing competition in Malta’s product andlabour markets using a dynamic stochastic general equilibrium (DSGE)
model.” (Micallef, 2019); “This model, together with its future extensions,is meant to be used as a complement to existing policy analysis tools
available at the Central Bank of Malta.”“NEMO has been used to identify the sources of business cycle fluctuationsin Norway, to conduct scenario analysis, to produce macroeconomic
forecasts, and to conduct monetary policy analysis.”; “Starting from 2018,the model has also been used for macro-prudential stress testing.” (Kravik
and Mimir, 2019)“NZSIM provides the platform for the Bank’s medium term economic
analysis and scenario testing during the monetary policy making process.”(Austin and Reid, 2017)“The main objective of this model is to conduct policy analysis, namely
forecast and simulations conditional on the behavior of monetary (and/or
fiscal) policy. Also, the model structure can be used to decompose
macroeconomic variables on the factors that explain their fluctuations.”(Florian and Montoro, 2009)“BSP’s DSGE model acts as a complement to existing models used by the
BSP for policy simulation.” (Reyes et al., 2017)“In 2009, a team consisting of the authors of this paper developed a newversion of the model, called SOEPL-2009 which in 2010 is to be used to obtainroutine mid-term forecasts of the inflation processes and the economic
trends, supporting and supplementing the traditional structural
macroeconometric model and experts’ forecasts applied so far.”; “We passthe DSGE SOEPL-2009 model for use, with a view to considering andanalyzing other interpretation and understanding of economic processes
than that proposed by the traditional models. Additionally, systematic workwith the model (preparing forecasts and analyses of their accuracy,
simulation experiments and analytical works) may reveal issues andproblems that will have to be solved.” (Grabek et al., 2011)“… the model is used to assess the impact of a number of shocks that
played an important role in Portugese economic developments,…”; “…
provide well-grounded support for structural reforms in Portugal,…”; “…
evaluate the impact of fiscal stimulus in a small open economy within amonetary union,…”; “used to show that a fiscal consolidation strategy
based on a permanent reduction in Government expenditure increases thelong-run level of output, private consumption and welfare, at the cost of
short-run welfare losses and output reduction.”; “evaluate the size of shortrun fiscal multipliers associated with fiscal consolidation under two distinct
alternative scenarios, viz “normal times” and “crisis times.”” (Almeida etal., 2013)“Regarding its role in policy making at the NBS, the DSGE model is to be
used mainly as a policy analysis tool rather than for forecasting…” (Djukic
et al., 2017)“The National Bank of Slovakia, as a member of the Eurosystem,
participates in policy discussions covering the entire euro area. While its

30

SWE

Sveriges
Riksbank

THA

Bank ofThailandUSA

FederalReserve Board

USA

FederalReserve Bankof New YorkUSA

FederalReserve Bankof Chicago

main objective is still to evaluate the effects of different policies andimpacts of shocks in the Slovak economy, the Slovak central bank is now
more interested in the evaluation of these effects on the whole euro area.
This motivation leads us to develop a two-country model in which countries
form a monetary union.” (Senaj et al., 2010)“The model is used to produce macroeconomic forecasts, to constructalternative scenarios, and for monetary policy analysis.” (Adolfson et al.,
2013)
“The purpose of this DSGE model is to provide a coherent economic
interpretation of the workings of the Thai economy consistent withmicroeconomic foundation”; “We at the Bank of Thailand believe that, in
addition to providing state-of-the-art tools, DSGE models will help
stimulate central bank research, provide an effective framework formonetary policy analysis and forecasting, and promote further insights into
the workings of the economy.” (Tanboon, 2008)“… we note that the EDO model serves as a complement to the analyses
that are currently performed using existing large-scale econometric models,such as FRB/US model, as well as smaller, ad hoc models that we havefound useful for more specific questions.”; “In addition, the EDO model isdesigned to allow the straightforward consideration of factors not explicitly
modeled in the baseline version of the model.” (Chung et al., 2010); “In thispaper, we describe a new multicountry open economy SDGE model named“SIGMA” that we have developed as a quantitative tool for policy
analysis.” (Erceg et al., 2006); “In this paper, we use a DSGE model(SIGMA) to show that taking account of the expenditure composition of
U.S. trade in an empirically realistic way yields implications for the
responses of trade to shocks that are markedly different from those of a‘standard’ framework that abstracts from such compositional differences.”(Erceg et al., 2008)“The New York Fed DSGE model came to existence around 2004 as athree-equation New Keynesian model (see Sbordone et al., 2010). At thattime, the model was used for a variety of policy analysis exercises but not
for forecasting.”; “In mid-2010, the model began to be used internally forforecasting the U.S. economy, …”; “The model built in 2010, which is
described in some detail in Del Negro et al. (2013), continued to be themain workhorse for DSGE projections and policy analysis at the NY Fed
until the end of 2014.” (Cai et al., 2019)“The Chicago Fed dynamic stochastic general equilibrium (DSGE) model isused for policy analysis and forecasting at the Federal Reserve Bank ofChicago. This article describes its specification and estimation, its dynamic
characteristics and how it is used to forecast the US economy.” (Brave etal., 2012)31

Table A.3: List of Models, Associated Institutions and References(a) National Government
NameCountry
RBA-DSGE*AUS
SAMBA*BRA
BoC-GEM-FIN* CAN
BoC-GEMCAN
DSGE-CH*XMAS*
MAS
PATACON*
HUBERT3*
g3*

CHE
CHL
CHL
COL
CZE
CZE

GEAR*
(no name)
REMS1*

DEU
DNK
ESP

REMS
MEDEA*FiMOD*BEMOD
EP-DSGE*AINO2*AINOEDGEKOOMA*OMEGA3*
COMPASS*BoGGEM*(no name)
PUSKAS*(no name)
FIR-GEM*

Institution/Division
Reserve Bank of Australia, Econ. ResearchCentral Bank of Brazil
Bank of Canada Intl. Econ. Analysis Dept.
Bank of Canada Intl. Econ. Analysis
Dept.
Swiss National Bank
Central Bank of Chile
Central Bank of Chile
Bank of the Republic (Columbia)
Ministry of Finance of the Czech RepublicCzech National Bank

Main Reference
Rees et al. (2016)de Castro et al. (2015)de Resende et al. (2016)
Lalonde and Muir (2007)
Cuche-Curti et al. (2009)
Garcia et al. (2019)
Medina and Soto (2007)
Gonzalez et al. (2011)
Aliyev et al. (2014)
Andrle et al. (2009)

Deutsche Bundesbank (BUBA)
Gadatsch et al. (2016)
Danmarks Nationalbank
Pedersen (2016)
Spanish Ministry of Economy and Finance, joint Bosca and Ferri (2016)
with SEPG, MoFPA, and SEEAE
ESP
Spanish Ministry of Economy and Finance, joint Bosca et al. (2010)
with SEPG, MoFPA, and SEEAE
ESP
Economic Office of the President of Spain
Burriel et al. (2010)
ESP/DEU Banco de Espana, joint with Deutsche
Stähler and Thomas
Bundesbank(2012)
ESP
Banco de Espana
Andres et al. (2006)
EST
Bank of Estonia
Gelain and Kulikov(2009)FIN
Bank of Finland
Kilponen et al. (2016)
FIN
Bank of Finland
Kilponen and Ripatti(2006)FIN
Bank of Finland
Kortelainen (2002)
FIN
Ministry of Finance (Finland)
Elmgren (2017), Ministryof Finance (2013, code);
FRA
French Ministry for the Economy and Finance, Carton and Guyon
joint with DGTPE(2007)
GBR
Bank of England
Burgess et al. (2013) and
its accompanying
appendix
GRC
Bank of Greece
Papageorgiou (2014)
HKG
Hong Kong Monetary Authority, Research Dept. Cheng and Ho (2009)
HUN
Central Bank of Hungary
Jakab et al. (2010)
HUN
Office of Fiscal Council
Baksa et al. (2010)
IRL
Department of Finance (Ireland), joint with
Varthalitis (2019)
ESRI, Revenue Commissioners

32

(no name)
MOISE*
IGEM2*

ISL
ISR
ITA

ITA

Central Bank of Iceland
Bank of Israel, Research Dept.
Ministry of Economy & Finance, Dept. ofTreasury
Ministry of Economy & Finance, Dept. ofTreasury
Ministry of Economy & Finance, Dept. ofTreasury
Banca d'Italia

Seneca (2010)
Argov et al. (2012)
Annicchiarico et al.
(2016)
Annicchiarico et al.
(2013)
Annicchiarico et al.
(2017)
Bartocci et al. (2017)

IGEM

ITA

IGEM-PAITA

NAWM (Italyver.)*
IDEA-BIEAGLE(no name)

ITA

Banca d'Italia

Forni et al. (2010)

ITA

Banca d'Italia

M-JEM*(no name)

JPN
JPN

Bank of Japan Research Dept.
Government of Japan Cabinet Office ESRI

LSM2*
LSM
(no name)

LUX
LUX
LVA

Luxembourg Min. of Economy & Trade
Luxembourg Min. of Economy & Trade
Latvia Banca

EAGLE (Maltaver.)*MEDSEA(no name)

MLT

Central Bank of Malta, Research Dept.

Darracq Paries andNotarpietro (2008)
Fueki et al. (2016)
Matsumae and Hasumi(2016)Deak et al. (2012)
Deak et al. (2011)
Ajevskis and Vitola(2011)
Micallef (2019)

MLT
NLD

Central Bank of Malta, Research Dept.
De Nederlandsche Bank (DNB)NEMO*
NZSIM*KITT
MEGA-D*

NOR
NZL
NZL
PER

Norges Bank
Reserve Bank of New Zealand
Reserve Bank of New ZealandCentral Reserve Bank of Peru

BSP’s DSGE
model*SOEPL-2012*
SOEPL-2009
PESSOA*
R.E.M. 2.0*
(no name)
MUSE*
DSGE ModelSlovakia(no name)
RAMSES2*RAMSESBOT DSGE*

PHL

Central Bank of Philippines

Rapa (2016)
Lafourcade and Wind
(2012)
Kravik and Mimir (2019)
Kamber et al. (2016)
Benes (2010)
Florian and Montoro(2009)
McNelis et al. (2010)

POL
POL
POR
ROU
SRB
SVK
SVK

National Bank of Poland
National Bank of Poland
Banco de Portugal
National Bank of Romania
National Bank of Serbia
National Bank of Slovakia
National Bank of Slovakia

Grabek and Klos (2013)
Grabek et al. (2011)
Almeida et al. (2013)
Copaciu et al. (2015)
Djukic et al. (2017)
Senaj et al. (2010)
Zeman and Senaj (2009)

SVK
SWE
SWE
THA

Council for Budget Responsibility
Sveriges Riksbank, Mon. Policy Dept.
Sveriges Riksbank, Mon. Policy Dept.
Bank of Thailand

Mucka (2016)
Adolfson et al. (2013)
Christiano et al. (2011)
Tanboon (2008)

33

EDO
SIGMA
SWFF++*SWFF+
SWFF
NY Fed DSGE
Model
Chicago Fed
DSGE Model(no name)

USA
USA
USA
USA
USA
USA

Federal Reserve Board
Federal Reserve Board
Federal Reserve Bank of New York
Federal Reserve Bank of New York
Federal Reserve Bank of New York
Federal Reserve Bank of New York

Chung et al. (2010)
Erceg et al. (2008)
Cai et al. (2019)
Cai et al. (2019)
Del Negro et al. (2015)
Del Negro et al. (2013)

USA

Federal Reserve Bank of Chicago

Brave et al. (2012)

ZAF

South African Reserve Bank

duPlessis et al. (2014)(b) International Organization
Name
GIMF*

Institution
Reference
International Monetary Fund Research
Kumhof et al. (2010)
Dept.
MAPMODInternational Monetary Fund Research
Benes et al. (2014)
Dept.
GEM
International Monetary Fund ResearchEveraert and Schule (2008)
Dept.
GFM
International Monetary Fund Research
Botman et al. (2006)
Dept.
SIXMODInternational Monetary Fund Research(no docs)
Dept.
GM3-EMU*
European Commission DG ECFIN, joint Albonico et al. (2019)with JRCQUEST3
European Commission DG ECFIN
Ratto et al. (2009)
NAWM2*
European Central Bank
Coenen et al. (2018)
NAWM
European Central Bank
Christoffel et al. (2008)
NAGE
European Central Bank(no docs)
EAGLE-FLI
European Central Bank, joint with Bank of Bokan et al. (2018)
Italy, Bank of Portugal
EAGLE
European Central Bank, joint with Bank of Gomes et al. (2012)
Italy, Bank of Portugal
EIRE Mod*
European Stability Mechanism
Clancy and Merola (2016b)(no name)
Organization for Economic Cooperation Cacciatore et al. (2012)and Development
Note: * indicates the model is used in the model comparison exercise of Section 5.

34

Appendix Reference
Adolfson, Malin, Stefan Laséen, Lawrence Christiano, Mathias Trabandt, and Karl Walentin,(2013). “Ramses II - Model Description,” Sveriges Riksbank Occasional Paper Series,
12.
Ajevskis, Viktors, Kristine Vitola (2011). “Housing and Banking in a Small Open Economy
DSGE Model,” mimeo.
Aliyev, Ilkin, Bozena Bobková, Zbynek Štork (2014). “Extended DSGE model of the Czech
Economy,” Ministry of Finance of the Czech Republic Working Paper, No.1/2014.
Almeida, Vanda, Gabriela Castro, Paulo Júlio, Félix Ricardo Mourinho, and José R. Maria(2013). “Inside PESSOA - A Detailed Description of the Model,” Banco de Portugal
Working Papers, 16/2013, Lisboa.
Ambriško, Róbert, Jan Babecký, Jakub Ryšánek, Vilém Valenta (2015). “Assessing the
Impact of Fiscal Measures on the Czech Economy,” Economic Modelling, 44, 350357, Elsevier.
Andrés, Javier, Pablo Burriel, and Ángel Estrada (2006).“BEMOD: A DSGE Model for the
Spanish Economy and the Rest of the Euro Area,” Banco de Espana Research Paper,
No. WP-0631.
Andrle, Michal, Tibor Hlédik, Ondra Kameník, and Jan Vlcek (2009). “Implementing the New
Structural Model of the Czech National Bank,” CNB Working Paper Series, 2/2009.
Annicchiarico, Barbara, Claudio Battiati, Claudio Cesaroni, Fabio Di Dio, Francesco Felici(2017). “IGEM-PA: a Variant of the Italian General Equilibrium Model for Policy
Analysis,” Ministry of Economy and Finance Department of the Treasury Working
Paper, 2-May 2017.
Annicchiarico, Barbara, Fabio Di Dio, and Francesco Felici (2016). “IGEM II: A New Variant of
the Italian General Equilibrium Model,” Ministry of Economy and Finance Departmentof the Treasury Working Paper, 4-October 2016.
Argov, Eyal, Emanuel Barnea, Alon Binyamini, Eliezer Borenstein, David Elkayam, andIrit Rozenshtrom (2012). “MOISE: A DSGE Model for the Israeli Economy,” Bank ofIsrael Discussion Paper, 2012.06.
Austin, Neroli and Geordie Reid (2017). “NZSIM: A Model of the New Zealand Economy for
Forecasting and Policy Analysis,” The Reserve Bank of New Zealand Bulletin, 80(1),Reserve Bank of New Zealand.
Baksa, Daniel, Szilard Benk, and Zoltan M. Jakab (2010). “Does “The” Fiscal Multiplier Exist?
Fiscal and Monetary Reactions, Credibility and Fiscal Multipliers in Hungary,” Office of
Fiscal Council Working Paper, 3.
Bartocci, Anna, Lorenzo Burlon, Alessandro Notarpietro, and Massimiliano Pisani (2017).
“Macroeconomic Effects of Non-standard Monetary Policy Measures in the Euro Area:
The Role of Corporate Bond Purchases,” Bank of Italy Working Paper, 1136.
Benes, Jaromir (2010). “Macroeconomic Modeling and Inflation-Rate Forecasting at theReserve Bank of New Zealand,” Mathworks Technical Articles and Newsletters Newsand Notes, 91844v00.
Benes, Jaromir, Andrew Binning, Martin Fukac, Kirdan Lees, and Troy Matheson (2009).
“K.I.T.T. Inflation Targeting Technology,” Reserve Bank of New Zealand.
Bokan, Nikola, Andrea Gerali, Sandra Gomes, Pascal Jacquinot, and Massimiliani Pisani (2018).

35

“EAGLE-FLI: A Macroeconomic Model of Banking and Financial Interdependence inthe Euro Area,” Economic Modelling, 69, 249-280.
Bokan, Nikola, Lovorka Grgurić, Ivo Krznar, Maroje Lang (2009). “The Impact of the
Financial Crisis and Policy Responses in Croatia,” Croatian National Bank
Working Papers, W-22.
Boscá, José E., A. Díaz, Rafael Domenech, Javier Ferri, E. Pérez, Luis A. Puch (2010). “A
Rational Expectations Model for Simulation and Policy Evaluation of the Spanish
Economy,” SERIEs, 1(02-Jan), 135-169, Springer.
Boscá, José E., Javier Ferri (2016).“REMS1: Adding Financial Frictions and a Housing
Market to REMS,” FEDEA Studies on the Spanish Economy, eee2016-03.
Brave, Scott A., Jeffrey R. Campbell, Jonas D.M. Fisher, Alejandro Justiniano (2012). “The
Chicago Fed DSGE Model,” FRB of Chicago Working Paper, WP2012-02.
Bruha, Jan, Jaromir Tonner (2019).“Independent Monetary Policy Versus a Common
Currency: A Macroeconomic Analysis for the Czech Republic Through the Lens ofan Applied DSGE Model,” Czech National Bank Working Paper, 19/2018.
Burgess, Stephen, Emilio Fernandez-Corugedo, Charlotta Groth, Richard Harrison,Francesca Monti, Konstantinos Theodoridis, and Matt Waldron (2013). “The Bank ofEngland’s Forecasting Platform: COMPASS, MAPS, EASE and the Suite of Models,”Bank of England Working Paper, 471.
Burriel, Pablo, Jesús Fernández-Villaverde, and Juan F. Rubio-Ramírez (2010).“MEDEA: A
DSGE Model for the Spanish Economy,” SERIEs, 1(02-Jan), 175-243, Springer.
Cai, Michael, Marco Del Negro, Marc P. Giannoni, Abhi Gupta, Pearl Li, and Erica
Moszkowski, (2019). “DSGE Forecasts of the Lost Recovery,” International Journal of
Forecasting, 35(4), 1770-1789, Elsevier.
Carton, Benjamin and Thibault Guyon (2007). “Divergences de productivité en Union Monétaire
Présentation du Modèle Oméga3,” Direction Générale du Trésor et de la PolitiqueTechnical Report, 2007/08.
Castillo, Paul, Carlos Montoro, and Vicente Tuesta (2009).“Un Modelo de Equilibrio Generalcon Dolarización para la Economía Peruana,” Banco Central de Reserva del Peru
Documento de Trabajo, 2009-006.
Cheng, Michael and Wai-Yip A. Ho (2009). “A Structural Investigation into the Price and Wage
Dynamics in Hong Kong,” Hong Kong Monetary Authority Working Paper, 20/2009.
Christiano, Lawrence J., Mathias Trabandt, and Karl Walentin (2011). “Introducing Financial
Frictions and Unemployment into a Small Open Economy Model,” Journal of Economic
Dynamics and Control, 35(12), 1999-2041, Elsevier.
Chung, Hess T., Michael T. Kiley, and Jean-Philippe Laforte (2010). “Documentation of theEstimated, Dynamic, Optimization-based (EDO) Model of the US Economy: 2010
Version,” Finance and Economic Discussion Series, 2010-29, Division of Research &Statistics and Monetary Affairs, Federal Reserve Board.
Clancy, Daragh and Rossana Merola (2016). “Eire Mod: A DSGE Model for Ireland,” The
Economic and Social Review, 47(1), 1-31.
Copaciu, Mihai, Valeriu Nalban, and Cristian Bulete (2015). “REM 2.0-An Estimated DSGE
Model for Romania,” Dynare Working Paper Series, 48.
Cuche-Curti, Nicolas A., Harris Dellas, and Jean-Marc Natal (2009). “DSGE-CH: A Dynamic
Stochastic General Equilibrium Model for Switzerland,” Swiss National Bank
Economic Studies, 5, 2009.
36

Darracq Pariès, Matthieu and Alessandro Notarpietro (2008). “Monetary Policy and Housing
Prices in an Estimated DSGE Model for the US and the Euro Area,” European Central
Bank Working Paper Series, 972/2008.
de Castro, Marcos R., Solange N. Gouvea, Andre Minella, Rafael Santos, and Nelson F. SouzaSobrinho (2015). “SAMBA: Stochastic Analytical Model with a Bayesian Approach,”Brazilian Review of Econometrics, 35(2), 103-170.
de Resende, Carlos, Ali Dib, René Lalonde, and Nikita Perevalov (2016). “Countercyclical Bank
Capital Requirement and Optimized Monetary Policy Rules,” Emerging Markets Financeand Trade, 52(10), 2267-2291, Taylor & Francis.
Deák, Szabolcs, Lionel Fontagné, Marco Maffezzoli, and Massimiliano Marcellino (2012).
“LSM2: The Banking and Distribution Sectors in a DSGE Model for Luxembourg,”
Ministére de L’Économie et du Commerce Extérieur Perspectives de Politique
Économique, 24, June 2012.
Deák,Szabolcs, Lionel Fontagné, Marco Maffezzoli, and Massimiliano Marcellino (2011).
“LSM: a DSGE Model for Luxembourg,” Economic Modelling, 28(6), 2862-2872,
Elsevier.
Del Negro, Marco, Stefano Eusepi, Marc P. Giannoni, Argia M. Sbordone, Andrea Tambalotti,
Matthew Cocci, Raiden Hasegawa, and M. Henry Linder (2013).“The FRBNY DSGE
Model,” FRB of New York Staff Report, 647.
Del Negro, Marco, Marc P. Giannoni, and Frank Schorfheide (2015). “Inflation in the GreatRecession and New Keynesian Models,” American Economic Journal: Macroeconomics,
7(1), 168-96.
Djukić, Mirko, Tibor Hlédik, Jiří Polanský, Ljubica Trajčev, and Jan Vlček (2017). “A DSGE
Model with Financial Dollarization–the Case of Serbia,” CNB Working Paper Series,
2/2017.
du Plessis, Stan, Ben Smit, and Rudi Steinbach (2014). “A Medium-sized Open Economy DSGE
Model of South Africa,” South African Reserve Bank Working Paper Series, WP/14/04.
Elmgren, Peter G.V. (2018). “The Effects of Debt Stabilising Fiscal Rules in a Macroeconomic(DSGE) Model,” Hanken School of Economics.
Erceg, Christopher J., Luca Guerrieri, and Christopher Gust (2006). “SIGMA: A New Open
Economy Model for Policy Analysis,” International Journal of Central Banking, 2(1), 150.
Erceg, Christopher J., Luca Guerrieri, and Christopher Gust (2008). “Trade Adjustment and theComposition of Trade,” Journal of Economic Dynamics and Control, 32(8), 2622-2650,
Elsevier.
Florian, David and Carlos Montoro (2009). “Development of Mega-D: A DSGE Model for
Policy Analysis,” Central Reserve Bank of Peru, May 2009.
Forni, Lorenzo, Andrea Gerali, and Massimiliano Pisani (2010). “The Macroeconomics of Fiscal
Consolidations in a Monetary Union: The Case of Italy,” Bank of Italy Working Paper,
747.
Gadatsch, Niklas, Klemens Hauzenberger, and Nikolai Stähler (2016). “Fiscal Policy During the
Crisis: A Look on Germany and the Euro area with GEAR,” Economic Modelling, 52,
997-1016, Elsevier.
García, Benjamín, Sebastián Guarda, Markus Kirchner, and Rodrigo Tranamil (2019). “XMAS:An Extended Model for Analysis and Simulations,” Central Bank of Chile Working
Paper, 833.
37

Gelain, Paolo and Dmirty Kulikov (2009). “An Estimated Dynamic Stochastic General
Equilibrium Model for Estonia,” Eesti Pank Working Paper Series, 5/2009.
Gelain, Paolo and Dmitry Kulikov (2011). “An Estimated Dynamic Stochastic General
Equilibrium Model with Financial Frictions for Estonia,” Eastern European Economics,
49(5), 97-120, Taylor & Francis.
Gomes, Sandra, Pascal Jacquinot, Massimiliani Pisani (2018). “The EAGLE. A Model for
Policy Analysis of Macroeconomic Interdependence in the Euro Area,” Economic
Modelling, 29(5), 1686-1714.
González-Gómez, Andrés, Lavan Mahadeva, Juan D. Prada-Sarmiento, and Diego A. RodríguezGuzmán (2011). “Policy Analysis Tool Applied to Colombian Needs: PATACON Model
Description,” Borradores de Economía, 656, Banco de la República.
González-Gómez, Andrés, Martha López, Norberto Rodríguez, and Santiago Téllez (2014).
“Fiscal Policy in a Small Open Economy with Oil Sector and Non-Ricardian Agents,”
Revista Desarrollo y Sociedad, 73, 33-69, Universidad de los Andes.
Grabek, Grzegorz and Bohdan Klos (2013). “Unemployment in the Estimated New Keynesian
SOEPL-2012 DSGE model,” National Bank of Poland Working Paper, 144.
Grabek, Grzegorz, Bohdan Klos, and Grzegorz Koloch (2011). “SOEPL 2009-An EstimatedDynamic Stochastic General Equilibrium Model for Policy Analysis and Forecasting,”
National Bank of Poland Working Paper, 83.
Jakab, Zoltán M., Henrik Kucsera, Katalin Szilágyi, and Balász Világi (2010). “Optimal Simple
Monetary Policy Rules and Welfare in a DSGE Model for Hungary,” MNB Working
Papers, 2010/4.
Kamber, Gunes, Chris McDonald, Nick Sander, and Konstantinos Theodoridis (2016).
“Modelling the Business Cycle of a Small Open Economy: The Reserve Bank of New
Zealand's DSGE model,” Economic Modelling, 59, 546-569, Elsevier.
Kilponen, Juha, Seppo Orjasniemi, Antti Ripatti, and Fabio Verona (2016). “The Aino 2.0
Model,” Bank of Finland Research Discussion Paper, 16/2016.
Kilponen, Juha, Antti Ripatti, and Jouko Vilmunen (2004). “Aino: The Bank of Finland's New
Dynamic General Equilibrium Model of the Finnish Economy,” Bank of Finland
Bulletin, 3.2004, 71-79.
Kortelainen, Mika (2002). “Edge: A Model of the Euro Area with Applications to
Monetary Policy,” Bank of Finland Studies, E:23/2002.
Kravik, Erling M. and Yasin Mimir (2019). “Navigating with NEMO,” Norges Bank Staff Memo,5/2019.
Lafourcade, Pierre M. and Joris de Wind (2012). “Taking Trends Seriously in DSGE Models: An
Application to the Dutch Economy,” De Nederlandsche Bank Working Paper, 345.
Lalonde, René; Muir, Dirk(2007).“The Bank of Canada's Version of the Global Economy
Model (BoC-GEM),” Bank of Canada Ottawa.
Júlio, Paolo and José R. Maria (2017). “The Portuguese Post-2008 Period: A Narrative From an
Estimated DSGE Model,” Banco de Portugal Working Papers, 15/2017.
McNelis, Paul D., Eloisa T. Glindro, Ferdinand S. Co, Francicso G. Dakila Jr. (2010).
“Macroeconomic Model for Policy Analysis and Insight (A Dynamic Stochastic General
Equilibrium model for the Bangko Sentral ng Pilipinas),” Bangko Sentral ng Pilipinas.
Medina, Juan P. and Claudio Soto (2007). “The Chilean Business Cycles Through the Lens of a
Stochastic General Equilibrium Model,” Central Bank of Chile Working Papers, 457.
Micallef, Brian (2013). “Measuring the Effects of Structural Reforms in Malta: An Analysis
38

Using the EAGLE Model,” CBM Working Papers, WP/01/2013.
Mucka, Zuzana (2016). “Fiscal Policy Matters A New DSGE Model for Slovakia,” CBR
Discussion Paper, 1/2016.
Papageorgiou, Dimitris (2014). “BoGGEM: A Dynamic Stochastic General Equilibrium Modelfor Policy Simulations,” Bank of Greece Working Paper, 182/2014.
Pedersen, Jesper (2016). “An Estimated DSGE Model for Denmark with Housing, Banking, andFinancial Frictions,” Danmarks Nationalbank Working Papers, 108/2016.
Rapa, Noel (2016). “MEDSEA: A Small Open Economy DSGE Model for Malta,” CBM
Working Papers, WP/05/2016.
Rees, Daniel M., Penelope Smith, and Jamie Hall (2016). “A Multi‐sector Model of theAustralian Economy,” Economic Record, 92(298), 374-408.
Reyes, Celia M., Connie B. Dacuycuy, Michael R. M. Abrigo, Francis M. A. Quimba,
Sylwyn C. Calizo Jr., Zhandra C. Tam, and Lora K.C. Baje (2017). “A Review ofPhilippine Macroeconometric Models,” Philippine Institute for Development
Studies Discussion Paper Series, 2017-43.
Schwarzmüller, Tim and Nikolai Stähler (2013). “Reforming the Labor Market and Improving
Competitiveness: An Analysis for Spain Using FiMod,” SERIEs, 4(4), 437-471,
Springer.
Senaj, Matúš, Milan Výškrabka, and Juraj Zeman (2010). “MUSE: Monetary Union and Slovak
Economy Model,” Ekonomický časopis, 60(5), 435-459.
Seneca, Martin (2010). “A DSGE Model for Iceland,” Central Bank of Iceland Working Paper,
50/2010.
Snellman, Oliver (2019). “Evaluation of DSGE Model KOOMA with a Sign Restricted
Structural VAR Model,” Ministry of Finance Discussion Papers, 2019:62, Helsinki,
Finland.
Stähler, Nikolai and Carlos Thomas (2012). “FiMod—A DSGE Model for Fiscal Policy
Simulations,” Economic Modelling, 29(2), 239-261, Elsevier.
Tanboon, S. (2008). “The Bank of Thailand Structural Model for Policy Analysis,” Bank ofThailand Discussion Paper, DP/12/2008.
Varthalitis, Petros (2019). “FIR-GEM: A SOE-DSGE Model for Fiscal Policy Analysis in
Ireland,” ESRI Working Paper, 620, Dublin, Ireland.
Zeman, Juraj and Matúš Senaj (2009). “DSGE Model-Slovakia,” National Bank of Slovakia
Working Paper, 3(2009), 4-37.

39

Table 1: Comparison of DSGE Models and Other Macroeconometric Models
Model
DSGE Policy Model
Projection Model
Traditional Macro Model

PolicyAnalysis

Forecast

Cost todevelop

Cost tomaintain

A
C
B

C
B
C

C
A
D

B
A
D

Note: The table is constructed based on the discussion in Hjelm et al. (2015) as well as our own opinion: theletters refer to A (desirable/easy/low cost) to D (not adequate/hard/high cost).

40

Table 2: Distribution of Institutions Based on the Number of Models per Institution
Number of models
per institution

Number of
institutions(% of total)

1
2
3
4
5
Total # of inst.
Average # of models
per institutions

34 (58.6%)
16 (27.6%)
3 (5.2%)4 (6.9%)1 (1.7%)58 (100.0%)
1.66

of which own
multi-regional
DSGE modelof whichcollaborate
with othersof which nonDSGE models are
additionally used

0
5
1
2
1
9
2.89

1
5
2
1
1
10
2.60

19
6
2
2
1
30
1.67

Note: The sample includes 58 institutions that appear in our baseline sample of 84 models.

41

Table 3: Distribution of Documentations Based on the Number of Authors
Number of authors
per documentation

1
2
3
4
5
6 and upTot. # of docs
Ave. # of authors
per documentation

Published
between
2000-19(% of total)
29 (15%)52 (27%)51 (26%)38 (20%)10 (5%)
14 (7%)
194 (100%)
3.20

Up to
2004

2004 to20092010 to2014

2015 to
2019

2
2
2
1
0
0
7
2.29

5
17
18
6
2
0
48
2.65

9
22
20
12
3
8
74
3.26

13
11
11
19
5
6
65
3.65

Note: The sample includes 194 documentations that are associated with our baseline sample of 84
models.

42

Table 4: The Number of DSGE Policy Models by Region and Institution TypeCentral banks
Government agencies
Ministries
Other agencies

Total

EuropeAmerica

Asia/Oceania

ME/Africa

Total

32
16

14
0

7
1

2
0

55
17

12
4

0
0

0
1

0
0

12
5

48

14

8

2

72

Note: The sample includes 72 models that are owned by either central banks or government agencies(i.e. 12 models developed by international organizations are excluded). The term “Other agencies”
include statistical office, national head’s office, and independent fiscal institutions.

43

Table 5: Pairwise Correlation

Samples
Nonunitary IES
Habit formation
Heterogeneous household
Financial friction
Unemployment

Nonunitary
IES
1.00
-0.14
0.20
-0.11
-0.18

Habit
formation

Heterogeneous
HH

Financial
friction

Unemployment

1.00
-0.27
-0.26
0.18

1.00
-0.15
0.06

1.00
-0.07

1.00

Note: The sample includes 42 models that are used in the model comparison exercise in Section 5.

44

Figure 1: Development of Macroeconomic Models

Note: The vertical axis represents how much a model honors the theoretical foundation while thehorizontal axis represents how well the model fits the data. 1G, 2G, 3G, 4G represent the distinct
generations of macroeconomic models as defined in Fukac and Pagan (2016).

45

Figure 2: Stated Objectives of DSGE Policy Models(a) policy

29(b) forecast

415(c) interpret business cycle

8(d) understand intl linkages

5(e) complement other models

6(f) risk analysis

1 2(g) others

3
0

7

2

8

1

2

3

11
5

Central Bank

10

15

Government

20

25

30

35

40

45

International Organization

Note: The sample includes 58 policy institutions used in our baseline sample of 84 models. Multiple objectives
are allowed for a given institution. For more details on the individual institutions, see Appendix Table A.2.

46

Figure 3: Distribution of DSGE Models by Year and Type of Institution
40
35

4

30
8

25

4
7

20
4
2

15
10

17
12

5
0

23

3
〜2004
Central Bank

2005〜09
Government

2010〜142015〜19

International Organization

Note: The sample includes 84 models used in our baseline sample. The year is defined as the year of
publication of the main reference document.

47

Figure 4: Distribution of the IES Parameter(a) Overall Sample (N = 42)
30
25
20
15

29

10
5
0
0 to 0.3

4

2
2

3

0.3 to 0.5

0.5 to 0.7

0.7 to 0.9

Calibrated

Estimated

0.9 to 1.1

More than 1.1

11

0.9 to 1.1

More than 1.1(b) Nonunitary Values (N = 14)
4.5
4
3.5
3

2

2.5
2

4

1.5

3

1
0.5

2
1

0
0 to 0.3

0.3 to 0.5

0.5 to 0.7

0.7 to 0.9

Calibrated

Estimated

Note: The sample in panel (a) includes 42 models that are used in the model comparison exercise in
Section 5 (36 calibrated, 6 estimated). The samples in panel (b) include 14 models that assign
nonunitary values for IES (8 calibrated, 6 estimated).

48

Figure 5: Distribution of the Consumption Habit Parameter
17
15
13
9

11
9
6

7
5

4
7

3

5

1
‐1

3

2
0 to 0.2

0.2 to 0.4

0.4 to 0.6
Calibrated

0.6 to 0.8

0.8 to 1

Estimated

Note: The sample includes 36 models that apply non-zero habit parameter (15 calibrated, 21 estimated).

49

Figure 6: Distribution of the Hand-to-Mouth (“HtoM”) Households Share Parameter
8
7
6
5
4

2
7

3
2

4
3

1

2
1

0
0 to 0.1

0.1 to 0.2

0.2 to 0.3
Calibrated

0.3 to 0.4

More than 0.4

Estimated

Note: The sample includes 19 models that adopt the HtoM approach (16 calibrated, 3 estimated).

50

Figure 7: Distribution of the Frisch Labor Supply Elasticity

14
12

4

10
8
2
6

1

10

4
2

5

2
2

0
0 to 0.2

61
1

0.2 to 0.4 0.4 to 0.6 0.6 to 0.8
Calibrated

1
0.8 to 1

1 to 22
2 to 51MoreEstimated

Note: The sample includes 38 models that present Frisch elasticity (25 calibrated, 13 estimated).

51

Figure 8: Distribution of the Score Card
18
1

16
14

5

2

12

2

10
8

1
1

6
4

11

10

7

2
2

0
1

2

3
CB

Govt.

4

0
5

Intl.org

Note: The evaluation score is based on the following five questions with one point rewarded if theanswer is yes to the following five questions: (1) Does the model adopt nonunitary value for the
consumption IES? (2) Does the model allow habit formation in consumption? (3) Does the model
introduce heterogeneity in households? (4) Does the model incorporate financial frictions? (5) Does
the model include unemployment?

52