Simulated Maximum Likelihood Estimation Based On First-Order Conditions

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dc.contributor.author Keane, Michael en_US
dc.contributor.editor en_US
dc.date.accessioned 2010-05-28T09:52:53Z
dc.date.available 2010-05-28T09:52:53Z
dc.date.issued 2009 en_US
dc.identifier 2008007848 en_US
dc.identifier.citation Keane Michael 2009, 'Simulated Maximum Likelihood Estimation Based On First-Order Conditions', Wiley-Blackwell Publishing, Inc, vol. 50, no. 2, pp. 627-675. en_US
dc.identifier.issn 0020-6598 en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/9926
dc.description.abstract I describe a strategy for structural estimation that uses simulated maximum likelihood (SML) to estimate the structural parameters appearing in a model's first-order conditions (FOCs). Generalized method of moments (GMM) is often the preferred method for estimation of FOCs, as it avoids distributional assumptions on stochastic terms, provided all structural errors enter the FOCs additively, giving a single composite additive error. But SML has advantages over GMM in models where multiple structural errors enter the FOCs nonadditively. I develop new simulation algorithms required to implement SML based on FOCs, and I illustrate the method using a model of U.S. multinational corporations. en_US
dc.language en_US
dc.publisher Wiley-Blackwell Publishing, Inc en_US
dc.relation.isbasedon http://dx.doi.org/10.1111/j.1468-2354.2009.00543.x en_US
dc.title Simulated Maximum Likelihood Estimation Based On First-Order Conditions en_US
dc.parent International Economic Review en_US
dc.journal.volume 50 en_US
dc.journal.number 2 en_US
dc.publocation Malden en_US
dc.identifier.startpage 627 en_US
dc.identifier.endpage 675 en_US
dc.cauo.name BUS.Faculty of Business en_US
dc.conference Verified OK en_US
dc.for 140302 en_US
dc.personcode 998871 en_US
dc.percentage 100 en_US
dc.classification.name Econometric and Statistical Methods en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity ISI:000265425800012 en_US
dc.description.keywords NA en_US
dc.staffid en_US
dc.staffid 998871 en_US


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