Assessing model adequacy in possibly misspecified quantile regression
Possibly misspecified linear quantile regression models are considered. A measure for assessing the combined effect of several covariates on a certain conditional quantile function is proposed. The measure is based on an adaptation to quantile regression of the famous coefficient of determination originally proposed for mean regression, and compares a ‘reduced’ model to a ‘full’ model, both of which can be misspecified. An estimator of this measure is proposed and its asymptotic distribution is investigated both in the non-degenerate and the degenerate case. The finite sample performance of the estimator is studied through a number of simulation experiments. The proposed measure is also applied to a data set on body fat measures.
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- Joshua Angrist & Victor Chernozhukov & Ivan Fernandez-Val, 2004.
"Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure,"
NBER Working Papers
10428, National Bureau of Economic Research, Inc.
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