A James-Stein-type adjustment to bias correction in fixed effects panel models

This paper proposes a James-Stein-type (JS) adjustment to analytical bias correction in fixed effects panel models that suffer from the incidental parameters problem. We provide high-level conditions under which the infeasible JS adjustment leads to a higher-order MSE improvement over the bias-corre...

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Bibliographic Details
Published inEconometric reviews Vol. 41; no. 6; pp. 633 - 651
Main Author Ghanem, Dalia
Format Journal Article
LanguageEnglish
Published New York Taylor & Francis 12.07.2022
Taylor & Francis Ltd
Subjects
Adjustment
Autoregressive models
Bias
Econometrics
incidental parameters
Longitudinal studies
maximum likelihood
panel data
Parameters
repeated measures
shrinkage
Weighting
Within-subjects design
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Summary:This paper proposes a James-Stein-type (JS) adjustment to analytical bias correction in fixed effects panel models that suffer from the incidental parameters problem. We provide high-level conditions under which the infeasible JS adjustment leads to a higher-order MSE improvement over the bias-corrected estimator, and the former is asymptotically equivalent to the latter. To obtain a feasible JS adjustment, we propose a nonparametric bootstrap procedure to estimate the JS weighting matrix and provide conditions for its consistency. We apply the JS adjustment to two models: (1) the linear autoregressive model with fixed effects, (2) the nonlinear static fixed effects model. For each application, we employ Monte Carlo simulations which confirm the theoretical results and illustrate the finite-sample improvements due to the JS adjustment. Finally, the extension of the JS procedure to a more general class of models and other policy parameters are illustrated.
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ISSN:0747-4938
1532-4168
DOI:10.1080/07474938.2021.1996994