WEIGHTED AND TWO-STAGE LEAST SQUARES ESTIMATION OF SEMIPARAMETRIC TRUNCATED REGRESSION MODELS

Shakeeb Khan and Arthur Lewbel ()

Econometric Theory, 2007, vol. 23, issue 02, pages 309-347

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.This research was supported in part by the National Science Foundation through grant SBR-9514977 to A. Lewbel. The authors thank Thierry Magnac, Dan McFadden, Jim Powell, Richard Blundell, Bo Honor , Jim Heckman, Xiaohong Chen, and Songnian Chen for helpful comments. Any errors are our own.

Date: 2007
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Working Paper: Weighted and Two Stage Least Squares Estimation of Semiparametric Truncated Regression Models (2006)
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