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dc.contributor.author Khan, S
dc.contributor.author Lewbel, A
dc.date.accessioned 2010-06-28T18:50:37Z
dc.date.issued 2007-04-01
dc.identifier.citation Econometric Theory, 2007, 23 (2), pp. 309 - 347
dc.identifier.issn 0266-4666
dc.identifier.uri http://hdl.handle.net/10161/2573
dc.description.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.
dc.format.extent 309 - 347
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartof Econometric Theory
dc.relation.isversionof 10.1017/S0266466607070132
dc.title Weighted and two-stage least squares estimation of semiparametric truncated regression models
dc.type Journal Article
dc.department Economics
pubs.issue 2
pubs.organisational-group /Duke
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Economics
pubs.publication-status Published
pubs.volume 23
dc.identifier.eissn 1469-4360

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