Understanding the effect of measurement error on quantile regressions

Andrew Chesher

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.

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Year of publication:	May 5th 2017
Authors:	Chesher, Andrew
Publisher:	London : Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL
Subject:	measurement error \| parameter approximations \| quantile regression \| Statistischer Fehler \| Statistical error \| Regressionsanalyse \| Regression analysis \| Schätztheorie \| Estimation theory \| Messung \| Measurement