A Comparison of Two-Stage Estimators of Censored Regression Models
This paper presents a Monte Carlo comparison of the small-sample performance of subsample ordinary least squares, the Heckman-Lee two-stage estimator, and the robust estimator of Lee. Each estimator is considered under bivariate normal, t, and chi-square error structures. The estimates indicate that the Heckman-Lee and Lee estimators do not provide an unequivocal mean square error improvement upon subsample ordinary least squares in small samples. While effectively controlling for selectivity bias, the two-stage estimators suffer a substantial loss of small-sample precision relative to subsample ordinary least squares. Copyright 1991 by MIT Press.
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Volume (Year): 73 (1991)
Issue (Month): 1 (February)
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