Estimating Nonlinear DSGE Models by the Simulated Method of Moments

Francisco Ruge-Murcia

Cahiers de recherche from Centre interuniversitaire de recherche en économie quantitative, CIREQ

Abstract: This paper studies the application of the simulated method of moments (SMM) for the estimation of nonlinear dynamic stochastic general equilibrium (DSGE) models. Monte Carlo analysis is employed to examine the small-sample properties of SMM in specifications with different curvature. Results show that SMM is computationally efficient and delivers accurate estimates, even when the simulated series are relatively short. However, asymptotic standard errors tend to overstate the actual variability of the estimates and, consequently, statistical inference is conservative. A simple strategy to incorporate priors in a method of moments context is proposed. An empirical application to the macroeconomic effects of rare events indicates that negatively skewed productivity shocks induce agents to accumulate additional capital and can endogenously generate asymmetric business cycles.

Keywords: Monte-Carlo analysis; priors; perturbation methods; rare events; skewness (search for similar items in EconPapers)
JEL-codes: C11 C15 E2 (search for similar items in EconPapers)
Pages: 62 pages
Date: 2010
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Citations: View citations in EconPapers (11)

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Related works:
Working Paper: Estimating Nonlinear DSGE Models by the Simulated Method of Moments (2011)
Working Paper: Estimating Nonlinear DSGE Models by the Simulated Method of Moments (2010)
Working Paper: Estimating Nonlinear DSGE Models by the Simulated Method of Moments (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:mtl:montec:19-2010

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