Abstract:
Root-n-consistent estimators of the regression coe-cients in the linear censored
regression model under conditional quantile restrictions on the error terms were proposedby
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, andare designedto overcome this 8nite-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 8nite sample behavior of
the proposedmethod s with their one-step counterparts
