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BOOTSTRAP AND k-STEP BOOTSTRAP BIAS CORRECTIONS FOR THE FIXED EFFECTS ESTIMATOR IN NONLINEAR PANEL DATA MODELS

Published online by Cambridge University Press:  15 February 2016

Min Seong Kim  and
Yixiao Sun
Min Seong Kim
Affiliation:
Ryerson University
Yixiao Sun*
Affiliation:
UC San Diego
*
*Address correspondence to Yixiao Sun, Department of Economics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0508; e-mail: yisun@ucsd.edu.
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Abstract

Because of the incidental parameters problem, the fixed effects maximum likelihood estimator in a nonlinear panel data model is in general inconsistent when the time series length T is short and fixed. Even if T approaches infinity but at a rate not faster than the cross sectional sample size n, the fixed effects estimator is still asymptotically biased. This paper proposes using the standard bootstrap and k-step bootstrap to correct the bias. We establish the asymptotic validity of the bootstrap bias corrections for both model parameters and average marginal effects. Our results apply to static models as well as some dynamic Markov models. Monte Carlo simulations show that our procedures are effective in reducing the bias of the fixed effects estimator and improving the coverage accuracy of the associated confidence interval.

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Type
ARTICLES
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Econometric Theory , Volume 32 , Issue 6 , December 2016 , pp. 1523 - 1568
Copyright
Copyright © Cambridge University Press 2016 

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