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Methods to Estimate Dynamic Stochastic General Equilibrium Models

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Author Info
RUGE-MURCIA, Francisco J.

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Abstract

This paper employs the one-sector Real Business Cycle model as a testing ground for four different procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1 ) Maximum Likelihood, with and without measurement errors and incorporating Bayesian priors, 2) Generalized Method of Moments, 3) Simulated Method of Moments, and 4) Indirect Inference. Monte Carlo analysis indicates that all procedures deliver reasonably good estimates under the null hypothesis. However, there are substantial differences in statistical and computational efficiency in the small samples currently available to estimate DSGE models. GMM and SMM appear to be more robust to misspecification than the alternative procedures. The implications of the stochastic singularity of DSGE models for each estimation method are fully discussed.

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Paper provided by Universite de Montreal, Departement de sciences economiques in its series Cahiers de recherche with number 2003-23.

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Length: 46 pages
Date of creation: 2003
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Handle: RePEc:mtl:montde:2003-23

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Related research
Keywords: DSGE models; estimation methods; Monte Carlo analysis; stochastic sin- gularity; Bayesian iors;

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Find related papers by JEL classification:
E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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