10.18452/3859
He, Xuming
Liang, Hua
Quantile Regression Estimates for a Class of Linear and Partially Linear Errors-in-Variables Models
Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
2006
semiparametric model
Kernel
linear regression
errors-in-variables
regression quantile
330 Wirtschaft
17 Wirtschaft
Humboldt-Universität zu Berlin
Humboldt-Universität zu Berlin
2017-06-15
2017-06-15
2006-06-09
2006-06-09
http://edoc.hu-berlin.de/series/sfb-373-papers/1997-103/PDF/103.pdf
http://edoc.hu-berlin.de/18452/4511
urn:nbn:de:kobv:11-10064800
We consider the problem of estimating quantile regression coefficients in errors-in-variables models. When the error variables for both the response and the manifest variables have a joint distribution that is spherically symmetric but otherwise unknown, the regression quantile estimates based on orthogonal residuals are shown to be consistent and asymptotically normal. We also extend the work to partially linear models when the response is related to some additional covariate.