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Understanding the effect of measurement error on quantile regressions

Chesher, A; (2017) Understanding the effect of measurement error on quantile regressions. Journal of Econometrics , 200 (2) pp. 223-237. 10.1016/j.jeconom.2017.06.007. Green open access

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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.

Type: Article
Title: Understanding the effect of measurement error on quantile regressions
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jeconom.2017.06.007
Publisher version: http://doi.org/10.1016/j.jeconom.2017.06.007
Language: English
Additional information: © 2017 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Measurement error; Parameter approximations; Quantile regression
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/1555613
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