Iatrogenic specification error: A cautionary tale of cleaning data

• 成果类型: Article
• 署名作者: Bollinger, CR; Chandra, A
• 署名单位: University of Kentucky; National Bureau of Economic Research; IZA Institute Labor Economics; Dartmouth College
• 刊物名称: JOURNAL OF LABOR ECONOMICS
• ISSN/ISSBN: 0734-306X
• DOI: 10.1086/428028
• 发表日期: 2005
• 页码: 235-257
• 关键词: relative earnings

摘要

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.