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dc.contributor.authorAn, Yonghongen
dc.contributor.authorHu, Yingyaoen
dc.date.accessioned2010-03-19-
dc.date.accessioned2011-09-27T15:21:21Z-
dc.date.available2011-09-27T15:21:21Z-
dc.date.issued2009-
dc.identifier.urihttp://hdl.handle.net/10419/49889-
dc.description.abstractIt is widely admitted that the inverse problem of estimating the distribution of a latent variable X* from an observed sample of X, a contaminated measurement of X*, is ill-posed. This paper shows that measurement error models for self-reporting data are well-posed, assuming the probability of reporting truthfully is nonzero, which is an observed property in validation studies. This optimistic result suggests that one should not ignore the point mass at zero in the error distribution when modeling measurement errors in self-reported data. We also illustrate that the classical measurement error models may in fact be conditionally well-posed given prior information on the distribution of the latent variable X*. By both a Monte Carlo study and an empirical application, we show that failing to account for the property can lead to significant bias on estimation of distribution of X*.en
dc.language.isoengen
dc.publisher|aThe Johns Hopkins University, Department of Economics |cBaltimore, MDen
dc.relation.ispartofseries|aWorking Paper |x556en
dc.subject.ddc330en
dc.subject.keywordWell-poseden
dc.subject.keywordconditionally well-poseden
dc.subject.keywordill-poseden
dc.subject.keywordinverse problemen
dc.subject.keywordFredholm integral equationen
dc.subject.keyworddeconvolutionen
dc.subject.keywordmeasurement error modelen
dc.subject.keywordself-reported dataen
dc.subject.keywordsurvey dataen
dc.subject.stwSchätztheorieen
dc.subject.stwBefragungen
dc.subject.stwStatistischer Fehleren
dc.subject.stwTheorieen
dc.titleWell-posedness of measurement error models for self-reported data-
dc.typeWorking Paperen
dc.identifier.ppn612116069en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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