A Semi-Parametric Estimator for Censored Selection Models with Endogeneity

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dc.contributor.author LEE, M. J.
dc.contributor.author VELLA, Frank
dc.date.accessioned 2011-04-19T12:48:28Z
dc.date.available 2011-04-19T12:48:28Z
dc.date.issued 2006
dc.identifier.citation Journal of Econometrics, 2006, 130, 2, 235-252
dc.identifier.issn 0304-4076
dc.identifier.uri http://hdl.handle.net/1814/16536
dc.description.abstract We propose a semi-parametric least-squares estimator for a censored-selection (type 3 tobit) model under the mean independence of the outcome equation error u from the regressors given the selection indicator and its error term epsilon. This assumption is relatively weak in comparison to alternative estimators for this model and allows certain unknown forms of heteroskedasticity, an asymmetric error distribution, and an arbitrary relationship between the u and epsilon. The estimator requires only one-dimensional smoothing on the estimate of epsilon. We generalize the estimator to allow for an endogenous regressor whose equation contains an error omega related to it and discuss how this latter procedure can be adapted to two-wave panel censored-selection models with double selection indicators. In general, each additional endogeneity problem can be controlled for with an extra dimensional smoothing on the residual for the endogencity-origin error term. Our proposed estimators are root N-consistent and asymptotically normal. An empirical example based on estimating a wage equation for Australian female youth is provided to illustrate our approach. (c) 2005 Elsevier B.V. All rights reserved.
dc.language.iso en
dc.publisher Elsevier Science Sa
dc.subject censored model
dc.subject selection problem
dc.subject type 3 tobit
dc.subject panel data
dc.title A Semi-Parametric Estimator for Censored Selection Models with Endogeneity
dc.type Article
dc.identifier.doi 10.1016/j.jeconom.2004.11.001
dc.neeo.contributor LEE|M. J.|aut|
dc.neeo.contributor VELLA|Frank|aut|
dc.identifier.volume 130
dc.identifier.startpage 235
dc.identifier.endpage 252
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dc.identifier.issue 2


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