Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/64749
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dc.contributor.authorAltonji, Josephen_US
dc.contributor.authorIchimura, Hidehikoen_US
dc.contributor.authorOtsu, Taisukeen_US
dc.date.accessioned2012-10-16T13:17:50Z-
dc.date.available2012-10-16T13:17:50Z-
dc.date.issued2008en_US
dc.identifier.pidoi:10.1920/wp.cem.2008.2008en_US
dc.identifier.urihttp://hdl.handle.net/10419/64749-
dc.description.abstractWe present a simple way to estimate the effects of changes in a vector of observable variables X on a limited dependent variable Y when Y is a general nonseparable function of X and unobservables. We treat models in which Y is censored from above or below or potentially from both. The basic idea is to first estimate the derivative of the conditional mean of Y given X at x with respect to x on the uncensored sample without correcting for the effect of changes in x induced on the censored population. We then correct the derivative for the effects of the selection bias. We propose nonparametric and semiparametric estimators for the derivative. As extensions, we discuss the cases of discrete regressors, measurement error in dependent variables, and endogenous regressors in a cross section and panel data context.en_US
dc.language.isoengen_US
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen_US
dc.relation.ispartofseries|acemmap working paper |xCWP20/08en_US
dc.subject.ddc330en_US
dc.subject.stwSch├Ątztheorieen_US
dc.subject.stwTobit-Modellen_US
dc.subject.stwBiasen_US
dc.subject.stwNichtparametrisches Verfahrenen_US
dc.titleEstimating derivatives in nonseparable models with limited dependent variablesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn574284494en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US

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