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

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References

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Citations

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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. Jakob Grazzini & Matteo G. Richiardi, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," LABORatorio R. Revelli Working Papers Series 130, LABORatorio R. Revelli, Centre for Employment Studies.
  4. 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.
  5. Bernhard Herz & Stefan Hohberger, 2013. "Fiscal Policy, Monetary Regimes and Current Account Dynamics," Review of International Economics, Wiley Blackwell, vol. 21(1), pages 118-136, 02.

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