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dc.contributor.authorAn, Yonghongen_US
dc.contributor.authorHu, Yingyaoen_US
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_US
dc.publisherJohns Hopkins Univ., Dep. of Economics Baltimore, Md.en_US
dc.relation.ispartofseriesWorking papers // the Johns Hopkins University, Department of Economics 556en_US
dc.subject.keywordconditionally well-poseden_US
dc.subject.keywordinverse problemen_US
dc.subject.keywordFredholm integral equationen_US
dc.subject.keywordmeasurement error modelen_US
dc.subject.keywordself-reported dataen_US
dc.subject.keywordsurvey dataen_US
dc.subject.stwStatistischer Fehleren_US
dc.titleWell-posedness of measurement error models for self-reported dataen_US
dc.typeWorking Paperen_US
Appears in Collections:Working Papers, Department of Economics, The Johns Hopkins University

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