G2TT
来源类型Working Paper
规范类型报告
DOI10.3386/t0289
来源IDTechnical Working Paper 0289
Iatrogenic Specification Error: A Cautionary Tale of Cleaning Data
Christopher R. Bollinger; Amitabh Chandra
发表日期2003-03-10
出版年2003
语种英语
摘要It is common in empirical research to use what appear to be 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. This paper considers identification in a linear model when the dependent variable is mismeasured. The results examine the common practice of trimming and winsorizing to address the identification failure. In contrast to the physical and laboratory sciences, measurement error in social science data is likely to be more complex than simply additive white noise. We consider a general measurement error process which nests many processes including the additive white noise process and a contaminated sampling process. Analytic results are only tractable under strong distributional assumptions, but demonstrate that winsorizing and trimming are only solutions for a particular class of measurement error processes. Indeed, trimming and winsorizing may induce or exacerbate bias. We term this source of bias Iatrogenic' (or econometrician induced) error. The identification results for the general error process highlight other approaches which are more robust to distributional assumptions. Monte Carlo simulations demonstrate the fragility of trimming and winsorizing as solutions to measurement error in the dependent variable.
主题Econometrics ; Estimation Methods ; Data Collection
URLhttps://www.nber.org/papers/t0289
来源智库National Bureau of Economic Research (United States)
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条目标识符http://119.78.100.153/handle/2XGU8XDN/567155
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Christopher R. Bollinger,Amitabh Chandra. Iatrogenic Specification Error: A Cautionary Tale of Cleaning Data. 2003.
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