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Bias in Conditional and Unconditional Fixed Effects Logit Estimation

Published online by Cambridge University Press:  04 January 2017

Ethan Katz
Ethan Katz*
Affiliation:
Center for Basic Research in the Social Sciences, Harvard University, 34 Kirkland Street, Cambridge, Massachusetts 02138. e-mail: ekatz@post.harvard.edu
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Abstract

Fixed-effects logit models can be useful in panel data analysis, when N units have been observed for T time periods. There are two main estimators for such models: unconditional maximum likelihood and conditional maximum likelihood. Judged on asymptotic properties, the conditional estimator is superior. However, the unconditional estimator holds several practical advantages, and therefore I sought to determine whether its use could be justified on the basis of finite-sample properties. In a series of Monte Carlo experiments for T < 20, I found a negligible amount of bias in both estimators when T ≥ 16, suggesting that a researcher can safely use either estimator under such conditions. When T < 16, the conditional estimator continued to have a very small amount of bias, but the unconditional estimator developed more bias as T decreased.

Information

Type
Research Article
Information
Political Analysis , Volume 9 , Issue 4 , 2001 , pp. 379 - 384
Copyright
Copyright © 2001 by the Society for Political Methodology 

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