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Endogeneity in Probit Response Models

Published online by Cambridge University Press:  04 January 2017

David A. Freedman  and
Jasjeet S. Sekhon
David A. Freedman
Affiliation:
Department of Statistics, University of California, Berkeley, CA 94720-3860. e-mail: freedman@stat.berkeley.edu
Jasjeet S. Sekhon*
Affiliation:
Department of Political Science, University of California, Berkeley, CA 94720-1950
*
e-mail: sekhon@berkeley.edu (corresponding author)
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Abstract

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We look at conventional methods for removing endogeneity bias in regression models, including the linear model and the probit model. It is known that the usual Heckman two-step procedure should not be used in the probit model: from a theoretical perspective, it is unsatisfactory, and likelihood methods are superior. However, serious numerical problems occur when standard software packages try to maximize the biprobit likelihood function, even if the number of covariates is small. We draw conclusions for statistical practice. Finally, we prove the conditions under which parameters in the model are identifiable. The conditions for identification are delicate; we believe these results are new.

Information

Type
Research Article
Information
Political Analysis , Volume 18 , Issue 2 , Spring 2010 , pp. 138 - 150
Copyright
Copyright © The Author 2010. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: Derek Briggs, Allan Dafoe, Thad Dunning, Joe Eaton, Eric Lawrence, Walter Mebane, Jim Powell, Rocío Titiunik, and Ed Vytlacil made helpful comments. Errors and omissions remain the responsibility of the authors.

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