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Iatrogenic Specification Error : A Cautionary Tale of Cleaning Data

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dc.creator Bollinger, Christopher R.
dc.creator Chandra, Amitabh
dc.date 2004
dc.date.accessioned 2013-10-16T07:10:02Z
dc.date.available 2013-10-16T07:10:02Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/20328
dc.identifier ppn:384725759
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/20328
dc.description 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 general measurement error process that nests many plausible models. Analytic results demonstrate that winsorizing and trimming are only solutions for a narrow class of measurement error processes. 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. We term this source of bias ?Iatrogenic? (or econometrician induced) error. Monte Carlo simulations and empirical results from the Census PUMS data and 2001 CPS data demonstrate the fragility of trimming and winsorizing as solutions to measurement error in the dependent variable. Even on asymptotic variance and RMSE criteria, we are unable to find generalizable justifications for commonly used cleaning procedures.
dc.language eng
dc.publisher
dc.relation IZA Discussion paper series 1093
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject J1
dc.subject C1
dc.subject ddc:330
dc.subject measurement error models
dc.subject trimming
dc.subject winsorizing
dc.subject Modell-Spezifikation
dc.subject Statistischer Fehler
dc.subject Bias
dc.subject Theorie
dc.title Iatrogenic Specification Error : A Cautionary Tale of Cleaning Data
dc.type doc-type:workingPaper


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