Understanding the effect of measurement error on quantile regressions

被引：13

作者：

Chesher, Andrew ^{[1
,2
]}

机构：

[1] UCL, London, England

[2] CeMMAP, London, England

来源：

JOURNAL OF ECONOMETRICS | 2017年 / 200卷 / 02期

关键词：

Measurement error; Parameter approximations; Quantile regression;

D O I：

10.1016/j.jeconom.2017.06.007

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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

引用

页码：223 / 237

页数：15

共 30 条

[1] Quantile regression under misspecification, with an application to the US wage structure [J].