Simulated maximum likelihood estimation based on first-order conditions

Keane, MP

Simulated maximum likelihood estimation based on first-order conditions

Keane, MP

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Publication Type:: Journal Article
Citation:: International Economic Review, 2009, 50 (2), pp. 627 - 675
Issue Date:: 2009-05-01

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Field	Value	Language
dc.contributor.author	Keane, MP	en_US
dc.date.issued	2009-05-01	en_US
dc.identifier.citation	International Economic Review, 2009, 50 (2), pp. 627 - 675	en_US
dc.identifier.issn	0020-6598	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. © 2009 by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.	en_US
dc.relation.ispartof	International Economic Review	en_US
dc.relation.isbasedon	10.1111/j.1468-2354.2009.00543.x	en_US
dc.subject.classification	Economics	en_US
dc.title	Simulated maximum likelihood estimation based on first-order conditions	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	50	en_US
utslib.for	140302 Econometric and Statistical Methods	en_US
utslib.for	14 Economics	en_US
dc.location.activity	ISI:000265425800012	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	50	en_US

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. © 2009 by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/9926