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dc.contributor.authorRuge-Murcia, Francisco
dc.date.accessioned2011-04-19T19:15:39Z
dc.date.available2011-04-19T19:15:39Z
dc.date.issued2010-11
dc.identifier.urihttp://hdl.handle.net/1866/4833
dc.publisherUniversité de Montréal. Département de sciences économiques.fr
dc.subjectMonte-Carlo analysisen
dc.subjectpriorsen
dc.subjectperturbation methodsen
dc.subjectrare eventsen
dc.subjectskewnessen
dc.titleEstimating Nonlinear DSGE Models by the Simulated Method of Momentsen
dc.typeArticle
dcterms.abstractThis 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.en
dcterms.bibliographicCitationCahier de recherche ; #2010-10
dcterms.isPartOfurn:ISSN:0709-9231
dcterms.languageengen
UdeM.VersionRioxxVersion publiée / Version of Record


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