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Estimating the parameters of a small open economy DSGE model: identifiability and inferential validity

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  • Daniel O. Beltran
  • David Draper

Abstract

This paper estimates the parameters of a stylized dynamic stochastic general equilibrium model using maximum likelihood and Bayesian methods, paying special attention to the issue of weak parameter identification. Given the model and the available data, the posterior estimates of the weakly identified parameters are very sensitive to the choice of priors. We provide a set of tools to diagnose weak identification, which include surface plots of the log-likelihood as a function of two parameters, heat plots of the log-likelihood as a function of three parameters, Monte Carlo simulations using artificial data, and Bayesian estimation using three sets of priors. We find that the policy coefficients and the parameter governing the elasticity of labor supply are weakly identified by the data, and posterior predictive distributions remind us that DSGE models may make poor forecasts even when they fit the data well. Although parameter identification is model- and data-specific, the lack of identification of some key structural parameters in a small-scale DSGE model such as the one we examine should raise a red flag to researchers trying to estimate--and draw valid inferences from--large-scale models featuring many more parameters.

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Bibliographic Info

Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 955.

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Date of creation: 2008
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Handle: RePEc:fip:fedgif:955

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Related research

Keywords: Econometric models ; Bayesian statistical decision theory ; Macroeconomics;

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References

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  1. Fabio Canova & Luca Sala, 2006. "Back to square one: identification issues in DSGE models," Computing in Economics and Finance 2006 196, Society for Computational Economics.
  2. Ruge-Murcia, Francisco J., 2007. "Methods to estimate dynamic stochastic general equilibrium models," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2599-2636, August.
  3. Michiel Evers & Ruud A. de Mooij & Daniel J. van Vuuren, 2006. "What explains the Variation in Estimates of Labour Supply Elasticities?," Tinbergen Institute Discussion Papers 06-017/3, Tinbergen Institute.
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  5. Andrew T. Levin & Alexei Onatski & John Williams & Noah M. Williams, 2006. "Monetary Policy Under Uncertainty in Micro-Founded Macroeconometric Models," NBER Chapters, in: NBER Macroeconomics Annual 2005, Volume 20, pages 229-312 National Bureau of Economic Research, Inc.
  6. Harrigan, James, 1993. "OECD imports and trade barriers in 1983," Journal of International Economics, Elsevier, vol. 35(1-2), pages 91-111, August.
  7. Frank Smets & Raf Wouters, 2002. "An estimated dynamic stochastic general equilibrium model of the euro area," Working Paper Research 35, National Bank of Belgium.
  8. Roger E. A. Farmer & Andreas Beyer, 2004. "On the Indeterminacy of New Keynesian Economics," 2004 Meeting Papers 187, Society for Economic Dynamics.
  9. Stephen G. Cecchetti & Michael Ehrmann, 2002. "Does Inflation Targeting Increase Output Volatility?: An International Comparison of Policymakers' Preferences and Outcomes," Central Banking, Analysis, and Economic Policies Book Series, in: Norman Loayza & Klaus Schmidt-Hebbel & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Series (ed.), Monetary Policy: Rules and Transmission Mechanisms, edition 1, volume 4, chapter 9, pages 247-274 Central Bank of Chile.
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  11. Onatski, Alexei & Stock, James H., 2002. "Robust Monetary Policy Under Model Uncertainty In A Small Model Of The U.S. Economy," Macroeconomic Dynamics, Cambridge University Press, vol. 6(01), pages 85-110, February.
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  13. BOUAKEZ, Hafed & CARDIA Emanuela & RUGE-MURCIA, Francisco, 2005. "The Transmission of Monetary Policy in a Multi-Sector Economy," Cahiers de recherche 2005-16, Universite de Montreal, Departement de sciences economiques.
  14. Nicoletta Batini & Alejandro Justiniano & Paul Levine & Joseph Pearlman, 2004. "Robust Inflation-Forecast-Based Rules to Shield against Indeterminacy," School of Economics Discussion Papers 0804, School of Economics, University of Surrey.
  15. Frank Smets & Raf Wouters, 2007. "Shocks and Frictions in US Business Cycles : a Bayesian DSGE Approach," Working Paper Research 109, National Bank of Belgium.
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Cited by:
  1. Guillermo Escudé, 2010. "Dynamic Stochastic General Equilibrium Models (DSGE): An Introduction," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(59), pages 25-79, July - Se.
  2. Melecky, Martin, 2010. "Macroeconomic Dynamics in Macedonia and Slovakia: Structural Estimation and Comparison," MPRA Paper 19863, University Library of Munich, Germany.
  3. Hohberger, Stefan & Herz, Bernhard, 2012. "Fiscal Policy, Monetary Regimes and Current Account Dynamics," Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 66054, Verein für Socialpolitik / German Economic Association.
  4. Grazzini, Jakob & Richiardi, Matteo, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201335, University of Turin.
  5. Petre Caraiani, 2009. "An Estimation of Output Gap in Romanian Economy Using the DSGE Approach," Prague Economic Papers, University of Economics, Prague, vol. 2009(4), pages 366-379.

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