Methods to Estimate Dynamic Stochastic General Equilibrium Models

Francisco Ruge-Murcia

Cahiers de recherche from Universite de Montreal, Departement de sciences economiques

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.

Keywords: DSGE models; estimation methods; Monte Carlo analysis; stochastic sin- gularity; Bayesian iors (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 C32 E13 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2003
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (38)

Downloads: (external link)
http://hdl.handle.net/1866/512 (application/pdf)

Related works:
Journal Article: Methods to estimate dynamic stochastic general equilibrium models (2007)
Working Paper: Methods to Estimate Dynamic Stochastic General Equilibrium Models (2004)
Working Paper: Methods to Estimate Dynamic Stochastic General Equilibrium Models (2003)
Working Paper: Methods to Estimate Dynamic Stochastic General Equilibrium Models (2002)
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Persistent link: https://EconPapers.repec.org/RePEc:mtl:montde:2003-23

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