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.
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Article provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 23 (2007) Issue (Month): 02 (April) Pages: 309-347 Download reference. The following formats are available: HTML
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