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Iatrogenic Specification Error: A Cautionary Tale Of Cleaning Data, Christopher R. Bollinger, Amitabh Chandra
Iatrogenic Specification Error: A Cautionary Tale Of Cleaning Data, Christopher R. Bollinger, Amitabh Chandra
Dartmouth Scholarship
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 pl ausible 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 …