Quantile regression estimates for a class of linear and partially linear errors-in-variables models
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.
|Date of creation:||1997|
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- Liang, Hua & Härdle, Wolfgang & Carroll, Raymond J., 1997. "Large sample theory in a semiparametric partially linear errors-in-variables models," SFB 373 Discussion Papers 1997,27, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- Hua, Liang & Ping, Cheng, 1993. "Second order asymptotic efficiency in a partial linear model," Statistics & Probability Letters, Elsevier, vol. 18(1), pages 73-84, August.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- He, Xuming & Shi, Peide, 1996. "Bivariate Tensor-Product B-Splines in a Partly Linear Model," Journal of Multivariate Analysis, Elsevier, vol. 58(2), pages 162-181, August.
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