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dc.contributor.authorAguirregabiria, Victor
dc.contributor.authorMarcoux, Mathieu
dc.date.accessioned2019-09-24T15:09:03Z
dc.date.available2019-09-24T15:09:03Z
dc.date.issued2019-09
dc.identifier.urihttp://hdl.handle.net/1866/22366
dc.publisherUniversité de Montréal. Département de sciences économiques.fr
dc.subjectDynamic discrete gamefr
dc.subjectNested pseudo-likelihoodfr
dc.subjectFixed point algorithmsfr
dc.subjectConvergencefr
dc.subjectConvergence selection biasfr
dc.titleImposing equilibrium restrictions in the estimation of dynamic discrete gamesfr
dc.typeArticlefr
dc.contributor.affiliationUniversité de Montréal. Faculté des arts et des sciences. Département de sciences économiques
dcterms.abstractImposing equilibrium restrictions provides substantial gains in the estimation of dynamic discrete games. Estimation algorithms imposing these restrictions – MPEC, NFXP, NPL, and variations – have different merits and limitations. MPEC guarantees local convergence, but requires the computation of high-dimensional Jacobians. The NPL algorithm avoids the computation of these matrices, but – in games – may fail to converge to the consistent NPL estimator. We study the asymptotic properties of the NPL algorithm treating the iterative procedure as performed in finite samples. We find that there are always samples for which the algorithm fails to converge, and this introduces a selection bias. We also propose a spectral algorithm to compute the NPL estimator. This algorithm satisfies local convergence and avoids the computation of Jacobian matrices. We present simulation evidence illustrating our theoretical results and the good properties of the spectral algorithm.fr
dcterms.isPartOfurn:ISSN:0709-9231
dcterms.languageengfr
UdeM.VersionRioxxVersion publiée / Version of Recordfr
oaire.citationTitleCahier de recherche
oaire.citationIssue2019-08


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