Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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 Honoré, Jim Heckman, Xiaohong Chen, and Songnian Chen for helpful comments. Any errors are our own.