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SEMIPARAMETRIC ESTIMATION OF NONSTATIONARY CENSORED PANEL DATA MODELS WITH TIME VARYING FACTOR LOADS

Published online by Cambridge University Press:  14 May 2008

Songnian Chen and
Shakeeb Khan
Songnian Chen*
Affiliation:
Hong Kong University of Science and Technology and National University of Singapore
Shakeeb Khan
Affiliation:
Duke University
*
Address correspondence to Songnian Chen, Department of Economics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; e-mail: snchen@ust.hk
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Abstract

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We propose an estimation procedure for a semiparametric panel data censored regression model in which the error terms may be subject to general forms of nonstationarity. Specifically, we allow for heteroskedasticity over time and a time varying factor load on the individual specific effect. Empirically, estimation of this model would be of interest to explore how returns to unobserved skills change over time—see, e.g., Chay (1995, manuscript, Princeton University) and Chay and Honoré (1998, Journal of Human Resources 33, 4–38). We adopt a two-stage procedure based on nonparametric median regression, and the proposed estimator is shown to be $\sqrt{n}$ -consistent and asymptotically normal. The estimation procedure is also useful in the group effect setting, where estimation of the factor load would be empirically relevant in the study of the intergenerational correlation in income, explored in Solon (1992, American Economic Review 82, 393–408; 1999, Handbook of Labor Economics, vol. 3, 1761–1800) and Zimmerman (1992, American Economic Review 82, 409–429).

Information

Type
Research Article
Information
Econometric Theory , Volume 24 , Issue 5 , October 2008, pp. 1149 - 1173
Copyright
Copyright © Cambridge University Press 2008

References

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