The impact of covariate measurement error 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 being the distribution of the error free covariate. Exact calculations probe the accuracy of the approximation. The order of the approxiamtion error is unchanged if the error free covariate density is replaced by the error contaminated density. It is then possible to use the approximation to investigate the sensitivity of estimates to varying amounts of measurement error.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Publisher Info
Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number
CWP02/01.
Length: 30 pp. Date of creation: Jul 2001 Date of revision: Handle: RePEc:ifs:cemmap:02/01
Contact details of provider: Postal: The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE Phone: (+44) 020 7291 4800 Fax: (+44) 020 7323 4780 Email: Web page: http://cemmap.ifs.org.uk
Order Information: Postal: The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE Email:
For technical questions regarding this item, or to correct its listing, contact: (Emma Hyman).
Related research
Keywords:
Other versions of this item:
This paper has been announced in the following NEP Reports:
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)