Two-step estimation of semiparametric censored regression models
Repository Usage Stats
Root-n-consistent estimators of the regression coefficients in the linear censored regression model under conditional quantile restrictions on the error terms were proposed by Powell (Journal of Econometrics 25 (1984) 303-325, 32 (1986a) 143-155). While those estimators have desirable asymptotic properties under weak regularity conditions, simulation studies have shown these estimators to exhibit a small sample bias in the opposite direction of the least squares bias for censored data. This paper introduces two-step estimators for these models which minimize convex objective functions, and are designed to overcome this finite-sample bias. The paper gives regularity conditions under which the proposed two-step estimators are consistent and asymptotically normal; a Monte Carlo study compares the finite sample behavior of the proposed methods with their one-step counterparts. © 2001 Elsevier Science S.A. All rights reserved.
Published Version (Please cite this version)10.1016/S0304-4076(01)00040-9
Publication InfoKhan, S; & Powell, JL (2001). Two-step estimation of semiparametric censored regression models. Journal of Econometrics, 103(1-2). pp. 73-110. 10.1016/S0304-4076(01)00040-9. Retrieved from https://hdl.handle.net/10161/1910.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
More InfoShow full item record
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