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https://hdl.handle.net/10419/20328
Kompletter Metadatensatz
DublinCore-Feld | Wert | Sprache |
---|---|---|
dc.contributor.author | Bollinger, Christopher R. | en |
dc.contributor.author | Chandra, Amitabh | en |
dc.date.accessioned | 2009-01-28T16:13:18Z | - |
dc.date.available | 2009-01-28T16:13:18Z | - |
dc.date.issued | 2004 | - |
dc.identifier.uri | http://hdl.handle.net/10419/20328 | - |
dc.description.abstract | In 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.iso | eng | en |
dc.publisher | |aInstitute for the Study of Labor (IZA) |cBonn | en |
dc.relation.ispartofseries | |aIZA Discussion Papers |x1093 | en |
dc.subject.jel | J1 | en |
dc.subject.jel | C1 | en |
dc.subject.ddc | 330 | en |
dc.subject.keyword | measurement error models | en |
dc.subject.keyword | trimming | en |
dc.subject.keyword | winsorizing | en |
dc.subject.stw | Modell-Spezifikation | en |
dc.subject.stw | Statistischer Fehler | en |
dc.subject.stw | Bias | en |
dc.subject.stw | Theorie | en |
dc.title | Iatrogenic Specification Error : A Cautionary Tale of Cleaning Data | - |
dc.type | Working Paper | en |
dc.identifier.ppn | 384725759 | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
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