Small sample performance of parameter estimators for tobit modesl with serial correlation*

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Abstract
For censored regression models with autocorrelated errors, the computation of maximum likelihood estimators becomes impractical when the sample contains long runs of limit observations. Pseudolikelihood estimators can be used in such cases. This paper reports the results of Monte Cario experiments designed to study the small sample performance of three pseudolikelihood estimators of the censored regression model with autocorrelated errors. These results suggest that the estimator proposed by Zeger and Brookmeyer as well as an alternative estimator described below behave markedly better than the consistent estimator obtained by simply ignoring serial correlation. A final experiment also suggests that the alternative estimator behaves well in large samples