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. © 2007 Cambridge University Press.
Type
Journal articlePermalink
https://hdl.handle.net/10161/2573Published Version (Please cite this version)
10.1017/S0266466607070132Publication Info
Khan, S; & Lewbel, A (2007). Weighted and two-stage least squares estimation of semiparametric truncated regression
models. Econometric Theory, 23(2). pp. 309-347. 10.1017/S0266466607070132. Retrieved from https://hdl.handle.net/10161/2573.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Shakeeb Khan
Professor of Economics
Professor Khan is on leave at Boston College for the 2016-17 academic year.Professor
Khan specializes in the fields of mathematical economics, statistics, and applied
econometrics. His studies have explored a variety of subjects from covariate dependent
censoring and non-stationary panel data, to causal effects of education on wage inequality
and the variables affecting infant mortality rates in Brazil. He was awarded funding
by National Science Foundation grants for his projects ent

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