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Two-Stage Conditional Maximum Likelihood Estimation of Econometric Models

Vuong, Quang H. (1984) Two-Stage Conditional Maximum Likelihood Estimation of Econometric Models. Social Science Working Paper, 538. California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20170919-135009248

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Abstract

Recent works on Maximum Likelihood (ML) estimation have focused on the behavior of the ML estimator when the model is possibly misspecified [Gourieroux, Monfort and Trognon (1984), Vuong (1983), White (1982, 1983a, b)]. This paper studies a general method, called two-stage conditional maximum likelihood (2SCML) estimation, for generating consistent estimates. In particular, asymptotic properties of 2SCML estimators are derived under correct and incorrect specification of the statistical model. Necessary and sufficient conditions for asymptotic efficiency of 2SCML estimators for all or some of the parameters are obtained. It is also argued that 2SCML estimators can readily be used to construct tests for exogeneity and model misspecification of the Hausman (1978) and White (1982) type. Examples are given to illustrate the applicability of the method. These include the linear simultaneous equation model, the simultaneous probit model and the simple Tobit model.


Item Type:Report or Paper (Working Paper)
Additional Information:I am greatly indebted to Kim Border, David Grether, Donald Lien and Douglas Rivers for helpful discussions and comments. I am also grateful to Doug Rivers for allowing me to use examples that have been worked out in two of our papers. Remaining errors are of course mine.
Group:Social Science Working Papers
Record Number:CaltechAUTHORS:20170919-135009248
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20170919-135009248
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:81574
Collection:CaltechAUTHORS
Deposited By: Jacquelyn Bussone
Deposited On:19 Sep 2017 21:08
Last Modified:21 Sep 2017 00:05

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