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.
Authors
![Andrew Chesher](/sites/default/files/styles/square_desktop/public/2022-07/Andrew_Chesher.jpg?itok=_w2THcSE)
Research Fellow University College London
Andrew is the Director of the ESRC Centre for Microdata Methods and Practice (cemmap) and Professor of Economics and Economic Measurement at UCL.
Journal article details
- DOI
- 10.1016/j.jeconom.2017.06.007
- Publisher
- Elsevier
- Issue
- Volume 200, October 2017, pages 223-237
Suggested citation
Chesher, A. (2017). 'Understanding the Effect of Measurement Error on Quantile Regressions' 200(2017), pp.223–237.
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