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

Shakeeb Khan and Arthur Lewbel

No 525, Boston College Working Papers in Economics from Boston College Department of Economics

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 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.

Keywords: Semiparametric; Truncated Regression; Heteroscedasticity; Latent Variable Models; Endogenous Regressors. (search for similar items in EconPapers)
JEL-codes: C13 C14 C25 (search for similar items in EconPapers)
Date: 2002-02-14, Revised 2006-09-04
New Economics Papers: this item is included in nep-ecm and nep-ifn
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: WEIGHTED AND TWO-STAGE LEAST SQUARES ESTIMATION OF SEMIPARAMETRIC TRUNCATED REGRESSION MODELS (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:bocoec:525

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