Monetary Policy Analysis with Potentially Misspecified Models

Policy analysis with potentially misspecified dynamic stochastic general equilibrium (DSGE) models faces two challenges: estimation of parameters that are relevant for policy trade-offs, and treatment of the deviations from the cross-equation restrictions. Using post-1982 US data, we study the robustness of the policy prescriptions from a state-of-the-art DSGE model with respect to two approaches to model misspecification pursued in the recent literature: (i) adding shocks to the DSGE model and/or generalizing the processes followed by these shocks; and (ii) explicit modeling of deviations from cross-equation restrictions (DSGE-VAR). (JEL C51, E13, E43, E52, E58)

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Modelling Monetary Policy

10.1093/oso/9780198854715.003.0007 ◽

2021 ◽

pp. 293-316

Author(s):

Juan Antonio Morales ◽

Paul Reding

Keyword(s):

Monetary Policy ◽

Policy Analysis ◽

Policy Decision ◽

Central Banks ◽

Dynamic Stochastic General Equilibrium ◽

Dsge Models ◽

Dynamic Stochastic ◽

Policy Decision Making

This last chapter deals with the toolbox that central banks use to design and implement their monetary policy strategy. Central banks develop various types of model, both for forecasting and for policy analysis. The chapter discusses the main characteristics of the models used, their strengths and limitations. It assesses how dynamic stochastic general equilibrium (DSGE) models are used for monetary policy analysis. Examples are provided on how they contribute to explore fundamental, long-term policy issues specific to LFDCs. The chapter also discusses the contribution of small semi-structural models which, though less strongly theory grounded than DSGE models, can be brought closer to the available data and are therefore possibly better suited to the context of LFDCs. Attention is also drawn to the key role of judgement as the indispensable complement, in monetary policy decision-making, to model-based policy analysis.

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A NOTE ON AN EXTENSION OF A CLASS OF SOLUTIONS TO DYNAMIC PROGRAMMING PROBLEMS ARISING IN ECONOMIC GROWTH

Macroeconomic Dynamics ◽

10.1017/s136510051000057x ◽

2011 ◽

Vol 16 (3) ◽

pp. 472-476 ◽

Cited By ~ 1

Author(s):

Jürgen Antony ◽

Alfred Maußner

Keyword(s):

Economic Growth ◽

Dynamic Programming ◽

General Equilibrium ◽

Dynamic Stochastic General Equilibrium ◽

Dsge Models ◽

Dsge Model ◽

Savings Rate ◽

Dynamics And Control ◽

Dynamic Stochastic ◽

And Control

This note extends the findings of Benhabib and Rusticchini [Journal of Economic Dynamics and Control 18, 807–813 (1994)], who provide a class of dynamic stochastic general equilibrium (DSGE) models whose solution is characterized by a constant savings rate. We show that this class of models may be interpreted as a standard–representative agent DSGE model with costly adjustment of capital.

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Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model

Studies in Nonlinear Dynamics & Econometrics ◽

10.1515/snde-2013-0061 ◽

2015 ◽

Vol 19 (2) ◽

Author(s):

Stelios Bekiros ◽

Alessia Paccagnini

Keyword(s):

Dynamic Stochastic General Equilibrium ◽

Dsge Models ◽

Dsge Model ◽

Time Data ◽

Vector Autoregressive ◽

Us Economy ◽

The Us ◽

Dynamic Stochastic ◽

Statistical Identification ◽

Density Forecasts

AbstractAlthough policymakers and practitioners are particularly interested in dynamic stochastic general equilibrium (DSGE) models, these are typically too stylized to be applied directly to the data and often yield weak prediction results. Very recently, hybrid DSGE models have become popular for dealing with some of the model misspecifications. Major advances in estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. In this study we introduce a Bayesian approach to estimate a novel factor augmented DSGE model that extends the model of Consolo et al. [Consolo, A., Favero, C.A., and Paccagnini, A., 2009. On the Statistical Identification of DSGE Models. Journal of Econometrics, 150, 99–115]. We perform a comparative predictive evaluation of point and density forecasts for many different specifications of estimated DSGE models and various classes of VAR models, using datasets from the US economy including real-time data. Simple and hybrid DSGE models are implemented, such as DSGE-VAR and tested against standard, Bayesian and factor augmented VARs. The results can be useful for macro-forecasting and monetary policy analysis.

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Constructing a Small-Region DSGE Model

ISRN Economics ◽

10.1155/2013/825862 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9

Author(s):

Kenichi Tamegawa

Keyword(s):

General Equilibrium ◽

Interest Rates ◽

Numerical Simulations ◽

National Economy ◽

Regional Economy ◽

Small Region ◽

Dynamic Stochastic General Equilibrium ◽

Dsge Models ◽

Dsge Model ◽

Dynamic Stochastic

This paper constructs a tractable dynamic stochastic general equilibrium (DSGE) model of a regional economy that is considered small because it does not affect its national economy. To examine properties of our small-region DSGE model, we conduct several numerical simulations. Notably, fiscal expansion in our model is larger than that in standard DSGE models. This is because the increase in regional output does not raise interest rates, and this leads to the crowding-in effects of investment.

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Bayesian Estimation of DSGE Models

10.23943/princeton/9780691161082.001.0001 ◽

2015 ◽

Cited By ~ 45

Author(s):

Edward P. Herbst ◽

Frank Schorfheide

Keyword(s):

Monte Carlo ◽

Sequential Monte Carlo ◽

Likelihood Function ◽

Academic Research ◽

Dynamic Stochastic General Equilibrium ◽

Computational Techniques ◽

Dsge Models ◽

Monte Carlo Techniques ◽

Dynamic Stochastic ◽

Theoretical Foundations

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. The book is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

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Decision between Public Investment and Public Consumption: A Policy Analysis

Global Economics Review ◽

10.31703/ger.2020(v-i).11 ◽

2020 ◽

Vol V (I) ◽

pp. 131-152

Author(s):

Muhammad Raashid ◽

Abdul Saboor ◽

Aneela Afzal

Keyword(s):

Public Investment ◽

Policy Decision ◽

Dynamic Stochastic General Equilibrium ◽

Dsge Model ◽

Structural Unemployment ◽

Government Investment ◽

Long Run ◽

Public Consumption ◽

Dynamic Stochastic ◽

Best Fit

This study aims to draw a policy decision between public investment and public consumption by designing a Dynamic Stochastic General Equilibrium (DSGE) model for the economy of Pakistan which is experiencing persistent shocks that have stressed the growth pattern. The DSGE model has a microeconomic foundation and justifies locus critics by envisioning an artificial economy. The model is evaluated and set to best fit for data through an exercise of moment matching. Government consumption shocks and Government Investment shocks are used to trace out the behaviour of the economy. The analysis confirms that Pakistan economy could go for capital formation through public investment but it results in compromised public consumption and structural unemployment. It is further concluded that the export base and long-run public investment programs are needed to achieve sustainable development in the economy.

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A DSGE-VAR Analysis for Tourism Development and Sustainable Economic Growth

Sustainability ◽

10.3390/su12093635 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3635

Author(s):

David Alaminos ◽

Ana León-Gómez ◽

José Ramón Sánchez-Serrano

Keyword(s):

Economic Growth ◽

Tourism Development ◽

Dynamic Stochastic General Equilibrium ◽

Long Term Effects ◽

Dsge Model ◽

External Effects ◽

Dynamic Stochastic ◽

Tourist Demand ◽

Sustainable Economic Growth

This paper aims to provide a better basis for understanding the transmission connection between tourism development and sustainable economic growth in the empirical scenario of International countries. In this way, we have applied the dynamic stochastic general equilibrium (DSGE) model in different countries in order to check the power of generalization of this framework to study the tourism development. Also, we extend this model to obtain the long-term effects of tourism development with confidence intervals. The influence of tourism development on sustainable economic growth is proved by our results and show the indirect consequences between tourist activity and other industries produced through the external effects of investment and human capital and public sector. Our study confirms that the DSGE technique can be a generalized model for the analysis of tourism development and, especially, can improve previous precision results with the DSGE-VAR model, where vector autoregression (VAR) is introduced in the DSGE model. The simulation results reveal even more than when the productivity of the economy in general enhances, as the current tourist demand increases in greater proportion than more than the national tourism demand. For its part, the consumption of domestic tourism rises more than the consumption of inbound tourism if the productivity of the tourism production enhances, but non-tourism prices decrease at a slower rate and tourism investment needs a longer time to recover to what is established.

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ASSET PRICING IN DYNAMIC STOCHASTIC GENERAL EQUILIBRIUM MODELS WITH INDETERMINACY

Macroeconomic Dynamics ◽

10.1017/s1365100507060373 ◽

2007 ◽

Vol 12 (1) ◽

pp. 50-71 ◽

Cited By ~ 1

Author(s):

NATALIA GERSHUN ◽

SHARON G. HARRISON

Keyword(s):

Asset Pricing ◽

General Equilibrium ◽

Returns To Scale ◽

Dynamic Stochastic General Equilibrium ◽

Increasing Returns ◽

Dsge Model ◽

Increasing Returns To Scale ◽

Local Indeterminacy ◽

Dynamic Stochastic ◽

The One

We explore asset pricing in the context of the one-sector Benhabib-Farmer-Guo (BFG) model with increasing returns to scale in production and compare our results with financial implications of the standard dynamic stochastic general equilibrium (DSGE) model. Our main goal is to determine the effects of local indeterminacy and the presence of sunspot shocks on asset pricing. We find that the BFG model does not adequately represent key stylized facts of U.S. capital markets and does not improve on the asset-pricing results obtained in the standard DSGE model.

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On DSGE Models

The Journal of Economic Perspectives ◽

10.1257/jep.32.3.113 ◽

2018 ◽

Vol 32 (3) ◽

pp. 113-140 ◽

Cited By ~ 37

Author(s):

Lawrence J. Christiano ◽

Martin S. Eichenbaum ◽

Mathias Trabandt

Keyword(s):

Financial Crisis ◽

General Equilibrium ◽

Policy Change ◽

Great Recession ◽

Relative Strength ◽

Dynamic Stochastic General Equilibrium ◽

Dsge Models ◽

The Great Recession ◽

Dynamic Stochastic ◽

Different Parts

The outcome of any important macroeconomic policy change is the net effect of forces operating on different parts of the economy. A central challenge facing policymakers is how to assess the relative strength of those forces. Economists have a range of tools that can be used to make such assessments. Dynamic stochastic general equilibrium (DSGE) models are the leading tool for making such assessments in an open and transparent manner. We review the state of mainstream DSGE models before the financial crisis and the Great Recession. We then describe how DSGE models are estimated and evaluated. We address the question of why DSGE modelers—like most other economists and policymakers—failed to predict the financial crisis and the Great Recession, and how DSGE modelers responded to the financial crisis and its aftermath. We discuss how current DSGE models are actually used by policymakers. We then provide a brief response to some criticisms of DSGE models, with special emphasis on criticism by Joseph Stiglitz, and offer some concluding remarks.

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