Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/20328 
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dc.contributor.authorBollinger, Christopher R.en
dc.contributor.authorChandra, Amitabhen
dc.date.accessioned2009-01-28T16:13:18Z-
dc.date.available2009-01-28T16:13:18Z-
dc.date.issued2004-
dc.identifier.urihttp://hdl.handle.net/10419/20328-
dc.description.abstractIn empirical research it is common practice to use sensible rules of thumb for cleaning data.Measurement error is often the justification for removing (trimming) or recoding (winsorizing)observations whose values lie outside a specified range. We consider a generalmeasurement error process that nests many plausible models. Analytic results demonstratethat winsorizing and trimming are only solutions for a narrow class of measurement errorprocesses. Indeed, for the measurement error processes found in most social-science data,such procedures can induce or exacerbate bias, and even inflate the variance estimates. Weterm this source of bias ?Iatrogenic? (or econometrician induced) error. Monte Carlosimulations and empirical results from the Census PUMS data and 2001 CPS datademonstrate the fragility of trimming and winsorizing as solutions to measurement error in thedependent variable. Even on asymptotic variance and RMSE criteria, we are unable to findgeneralizable justifications for commonly used cleaning procedures.en
dc.language.isoengen
dc.publisher|aInstitute for the Study of Labor (IZA) |cBonnen
dc.relation.ispartofseries|aIZA Discussion Papers |x1093en
dc.subject.jelJ1en
dc.subject.jelC1en
dc.subject.ddc330en
dc.subject.keywordmeasurement error modelsen
dc.subject.keywordtrimmingen
dc.subject.keywordwinsorizingen
dc.subject.stwModell-Spezifikationen
dc.subject.stwStatistischer Fehleren
dc.subject.stwBiasen
dc.subject.stwTheorieen
dc.titleIatrogenic Specification Error : A Cautionary Tale of Cleaning Data-
dc.typeWorking Paperen
dc.identifier.ppn384725759en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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