Semiparametric estimation of nonstationary censored panel data models with time varying factor loads

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2008-10-01

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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 √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). © 2008 Cambridge University Press.

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10.1017/S0266466608080468

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Chen, S, and S Khan (2008). Semiparametric estimation of nonstationary censored panel data models with time varying factor loads. Econometric Theory, 24(5). pp. 1149–1173. 10.1017/S0266466608080468 Retrieved from https://hdl.handle.net/10161/2554.

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Khan

Shakeeb Khan

Professor of Economics

Professor Khan is on leave at Boston College for the 2016-17 academic year.

Professor Khan specializes in the fields of mathematical economics, statistics, and applied econometrics. His studies have explored a variety of subjects from covariate dependent censoring and non-stationary panel data, to causal effects of education on wage inequality and the variables affecting infant mortality rates in Brazil. He was awarded funding by National Science Foundation grants for his projects entitled, “Estimation of Binary Choice and Nonparametric Censored Regression Models” and “Estimation of Cross-Sectional and Panel Data Duration Models with General Forms of Censoring.” He has published numerous papers in leading academic journals, including such writings as, “Heteroskedastic Transformation Models with Covariate Dependent Censoring” with E. Tamer and Y. Shin; “The Identification Power of Equilibrium in Simple Games;” “Partial Rank Estimation of Duration Models with General Forms of Censoring” with E. Tamer; and more. He is currently collaborating with D. Nekipelov and J.L. Powell on the project, “Optimal Point and Set Inference in Competing Risk Models;” with A. Lewbel on, “Identification and Estimation of Stochastic Frontier Models;” and with E. Tamer on, “Conditional Moment Inequalities in Roy Models with Cross-Section and Panel Data.”


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