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

Published

Journal Article

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.

Full Text

Cited Authors

  • Chen, S; Khan, S

Published Date

  • October 1, 2008

Published In

Volume / Issue

  • 24 / 5

Start / End Page

  • 1149 - 1173

Electronic International Standard Serial Number (EISSN)

  • 1469-4360

International Standard Serial Number (ISSN)

  • 0266-4666

Digital Object Identifier (DOI)

  • 10.1017/S0266466608080468

Citation Source

  • Scopus