Abstract
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 where the dependent variable has values that 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 solutions for a narrow class of error processes. Indeed such procedures can induce or exacerbate bias. Monte Carlo simulations and empirical results demonstrate the fragility of cleaning. Even on root mean square error criteria, we cannot find generalizable justifications for these procedures.
Original language | English |
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Pages (from-to) | 235-257 |
Number of pages | 23 |
Journal | Journal of Labor Economics |
Volume | 23 |
Issue number | 2 |
DOIs | |
State | Published - Apr 2005 |
ASJC Scopus subject areas
- Industrial relations
- Economics and Econometrics