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Methods to Estimate Dynamic Stochastic General Equilibrium Models

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  • RUGE-MURCIA, Francisco J.

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 Centre interuniversitaire de recherche en économie quantitative, CIREQ in its series Cahiers de recherche with number 17-2003.

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Length: 46 pages
Date of creation: 2003
Date of revision:
Handle: RePEc:mtl:montec:17-2003

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Keywords: DSGE models; estimation methods; Monte Carlo analysis; stochastic singularity; Bayesian priors;

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