Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification

Econometrics

Ying-Ying Lee¹ 

[1] Department of Economics, University of Oxford, Manor Road Building, Manor Road, Oxford OX1 3UQ, UK

关键词: semiparametric efficiency bounds;    misspecification;    conditional quantile function;    conditional distribution function;    best linear approximation;   

DOI  :  10.3390/econometrics4010002

来源: mdpi

【 摘 要 】

Allowing for misspecification in the linear conditional quantile function, this paper provides a new interpretation and the semiparametric efficiency bound for the quantile regression parameter , i.e., the deviation of the conditional distribution function, evaluated at the linear quantile approximation, from the quantile level. The second result implies that the Koenker–Bassett estimator semiparametrically efficiently estimates the quantile regression parameter that produces parsimonious descriptive statistics for the conditional distribution. Therefore, quantile regression shares the attractive features of ordinary least squares: interpretability and semiparametric efficiency under misspecification.

【 授权许可】

CC BY   
© 2015 by the author; licensee MDPI, Basel, Switzerland.