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A NEW PANEL DATA TREATMENT FOR HETEROGENEITY IN TIME TRENDS

Published online by Cambridge University Press:  01 May 2012

Alois Kneip ,
Robin C. Sickles  and
Wonho Song
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Abstract

This paper introduces a new estimation method for arbitrary temporal heterogeneity in panel data models. The paper provides a semiparametric method for estimating general patterns of cross-sectional specific time trends. The methods proposed in the paper are related to principal component analysis and estimate the time-varying trend effects using a small number of common functions calculated from the data. An important application for the new estimator is in the estimation of time-varying technical efficiency considered in the stochastic frontier literature. Finite sample performance of the estimators is examined via Monte Carlo simulations. We apply our methods to the analysis of productivity trends in the U.S. banking industry.

Type
Research Article
Information
Econometric Theory , Volume 28 , Issue 3 , June 2012 , pp. 590 - 628
Copyright
Copyright © Cambridge University Press 2011

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Footnotes

Earlier versions of this paper under the title “On Estimating the Mixed Effects Model” were presented at North American Productivity Workshop, Toronto, June 2004; COMPSTAT 2004, Prague, August 2004; and the European Workshop of Efficiency and Productivity IX, Brussels, July 2005. The paper was also given at the 2005 Econometric Society World Congress, University College, London, and Michigan State, Rice University, and Syracuse University econometrics workshops. The authors thank participants at those conferences and workshops, particularly Peter Schmidt, Mahmoud El-Gamal, Yoosoon Chang, Joon Park, and Leopold Simar, for constructive criticisms and insights. We also thank three anonymous referees and editor Guido Kuersteiner for their insightful comments and criticisms that substantially strengthened the paper, and Levent Kutlu for his very crucial research assistance. The usual caveat applies. Song gratefully acknowledges financial support from Chung-Ang University through research grants in 2011.

References

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