Abstract:
Powell's (1984, Journal of Econometrics 25, 303}325) censored least absolute deviations
(CLAD) estimator for the censored linear regression model has been regarded as
a desirable alternative to maximum likelihood estimation methods due to its robustness
to conditional heteroskedasticity and distributional misspeci"cation of the error term.
However, the CLAD estimation procedure has failed in certain empirical applications
due to the restrictive nature of the &full rank' condition it requires. This condition can be
especially problematic when the data are heavily censored. In this paper we introduce
estimation procedures for heteroskedastic censored linear regression models with a much
weaker identi"cation restriction than that required for the LCAD, and which are #exible
enough to allow for various degrees of censoring. The new estimators are shown to have
desirable asymptotic properties and perform well in small-scale simulation studies, and
can thus be considered as viable alternatives for estimating censored regression models,
especially for applications in which the CLAD fails
