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Methods to Estimate Dynamic Stochastic General Equilibrium Models

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Author Info
Francisco J. Ruge-Murcia

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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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Publisher Info
Paper provided by Society for Economic Dynamics in its series 2004 Meeting Papers with number 83.

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Date of creation: 2004
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Handle: RePEc:red:sed004:83

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Related research
Keywords: DSGE Models; Monte-Carlo Analysis; Estimation Methods; Stochastic Singularity;

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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

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