Journal
JOURNAL OF ECONOMETRICS
Volume 200, Issue 2, Pages 223-237Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2017.06.007
Keywords
Measurement error; Parameter approximations; Quantile regression
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Funding
- ESRC [R0027836, RES-589-28-0001]
- Leverhulme Trust
- ESRC [ES/I034021/1, ES/M010147/1] Funding Source: UKRI
- Economic and Social Research Council [ES/I034021/1] Funding Source: researchfish
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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. (C) 2017 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license
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