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Semiparametric Estimation Of Nonstationary Censored Panel Data Models With Time Varying Factor Loads

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  • Chen, Songnian
  • Khan, Shakeeb

Abstract

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).

Suggested Citation

  • Chen, Songnian & Khan, Shakeeb, 2008. "Semiparametric Estimation Of Nonstationary Censored Panel Data Models With Time Varying Factor Loads," Econometric Theory, Cambridge University Press, vol. 24(5), pages 1149-1173, October.
    • Handle: RePEc:cup:etheor:v:24:y:2008:i:05:p:1149-1173_08
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    Cited by:

    1. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2016. "Identification of panel data models with endogenous censoring," Journal of Econometrics, Elsevier, vol. 194(1), pages 57-75.
    2. Choi, Jin-young & Lee, Myoung-jae, 2019. "Twins are more different than commonly believed, but made less different by compensating behaviors," Economics & Human Biology, Elsevier, vol. 35(C), pages 18-31.
    3. Joachim Wagner, 2016. "From Estimation Results to Stylized Facts: Twelve Recommendations for Empirical Research in International Activities of Heterogeneous Firms," World Scientific Book Chapters, in: Microeconometrics of International Trade, chapter 15, pages 479-514, World Scientific Publishing Co. Pte. Ltd..
    4. David Powell & Joachim Wagner, 2010. "The Exporter Productivity Premium along the Productivity Distribution: First Evidence from a Quantile Regression Approach for Fixed Effects Panel Data Models," Working Paper Series in Economics 182, University of Lüneburg, Institute of Economics.
    5. Evans, Mary F. & Schaur, Georg, 2010. "A quantile estimation approach to identify income and age variation in the value of a statistical life," Journal of Environmental Economics and Management, Elsevier, vol. 59(3), pages 260-270, May.
    6. Carlos A. Flores & Alfonso Flores-Lagunes & Dimitrios Kapetanakis, 2014. "Lessons From Quantile Panel Estimation of the Environmental Kuznets Curve," Econometric Reviews, Taylor & Francis Journals, vol. 33(8), pages 815-853, November.
    7. Genya Kobayashi & Hideo Kozumi, 2012. "Bayesian analysis of quantile regression for censored dynamic panel data," Computational Statistics, Springer, vol. 27(2), pages 359-380, June.
    8. Xin, Kai & Zhang, ZhengYu & Zhou, YaHong & Zhu, PingFang, 2021. "Time-varying individual effects in a panel data probit model with an application to female labor force participation," Economic Modelling, Elsevier, vol. 95(C), pages 181-191.
    9. Karen X. Yan & Qi Li, 2018. "Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model," JRFM, MDPI, vol. 11(3), pages 1-10, August.
    10. Li, Tong & Oka, Tatsushi, 2015. "Set identification of the censored quantile regression model for short panels with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 363-377.

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