Semiparametric estimation of nonstationary censored panel data models with time varying factor loads
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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 √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.
Published Version (Please cite this version)10.1017/S0266466608080468
Publication InfoChen, S; & Khan, S (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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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 ent