Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189718 
Authors: 
Year of Publication: 
2017
Series/Report no.: 
cemmap working paper No. CWP19/17
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
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.
Subjects: 
measurement error
parameter approximations
quantile regression
JEL: 
C13
C14
C21
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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