Abstract:
This paper provides a root-n consistent, asymptotically normal weighted least
squares estimator of the coefficients in a truncated regression model+ The distribution
of the errors is unknown and permits general forms of unknown heteroskedasticity+
Also provided is an instrumental variables based two-stage least squares
estimator for this model, which can be used when some regressors are endogenous,
mismeasured, or otherwise correlated with the errors+ A simulation study
indicates that the new estimators perform well in finite samples+ Our limiting
distribution theory includes a new asymptotic trimming result addressing the
boundary bias in first-stage density estimation without knowledge of the support
boundary+
