Understanding the effect of measurement error on quantile regressions

Andrew Chesher

No CWP19/17, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: The impact of measurement error in explanatory variables on quantile regression functions is investigated using a small variance approximation. The approximation shows how the error contaminated and error free quantile regression functions are related. A key factor is the distribution of the error free explanatory variable. Exact calculations probe the accuracy of the approximation. The order of the approximation error is unchanged if the density of the error free explanatory variable is replaced by the density of the error contaminated explanatory variable which is easily estimated. It is then possible to use the approximation to investigate the sensitivity of estimates to varying amounts of measurement error.

Keywords: measurement error; parameter approximations; quantile regression. (search for similar items in EconPapers)
Date: 2017-05-10
New Economics Papers: this item is included in nep-ecm and nep-eff
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Citations: View citations in EconPapers (8)

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Related works:
Journal Article: Understanding the effect of measurement error on quantile regressions (2017)
Working Paper: Understanding the effect of measurement error on quantile regressions (2017)
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