 EFFICIENT ESTIMATION OF SEMIPARAMETRIC MODELS BY SMOOTHED MAXIMUM LIKELIHOODStephen R. Cosslett International Economic Review, 2007, vol. 48, issue 4, 1245-1272 Abstract: A smoothed likelihood function is used to construct efficient estimators for some semiparametric models that contain unknown density functions together with parametric index functions. Smoothing the likelihood makes maximization with respect to the unknown density functions more tractable. The method is used to show the efficiency gains from knowledge of population shares in three cases: (1) binary choice; (2) binary choice when only one outcome is sampled, supplemented by random sampling of the explanatory variables; and (3) linear regression, where the shares are defined by a threshold value of the dependent variable. Semiparametric efficiency is achieved both for parametric components and for a class of functionals of the error density. Copyright 2007 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association. Date: 2007 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ier:iecrev:v:48:y:2007:i:4:p:1245-1272 Ordering information: This journal article can be ordered from Access Statistics for this article International Economic Review is currently edited by Harold L. Cole More articles in International Economic Review from Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association 160 McNeil Building, 3718 Locust Walk, Philadelphia, PA 19104-6297. Contact information at EDIRC. |
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