Weighted and Two-Stage Least Squares Estimation of Semiparametric Truncated Regression Models

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+