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Estimation, Inference, and Specification Testing for Possibly Misspecified Quantile Regression Author info | Abstract | Publisher info | Download info | Related research | Statistics Halbert White (University of California, San Diego)
Tae-Hwan Kim (Yonsei University)
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To date the literature on quantile regression and least absolute deviation regression has assumed either explicitly or implicitly that the conditional quantile regression model is correctly specified. When the model is misspecified, confidence intervals and hypothesis tests based on the conventional covariance matrix are invalid. Although misspecification is a generic phenomenon and correct specification is rare in reality, there has to date been no theory proposed for inference when a conditional quantile model may be misspecified. In this paper, we allow for possible misspecification of a linear conditional quantile regression model. We obtain consistency of the quantile estimator for certain "pseudo-true" parameter values and asymptotic normality of the quantile estimator when the model is misspecified. In this case, the asymptotic covariance matrix has a novel form, not seen in earlier work, and we provide a consistent estimator of the asymptotic covariance matrix. We also propose a quick and simple test for conditional quantile misspecification based on the quantile residuals.
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Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number
2002-09.
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Date of creation: 01 Apr 2002Date of revision:
Handle: RePEc:cdl:ucsdec:2002-09Note: oai:cdlib1:Contact details of provider: Postal: 9500 Gilman Drive, La Jolla, CA 92093-0508 Phone: (858) 534-3383 Fax: (858) 534-7040 Web page: http://repositories.cdlib.org/ucsdecon/ More information through EDIRC
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Keywords: conditional quantile ; misspecification ; asymptotic normality ; asymptotic covariance matrix ; References listed on IDEAS 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.:
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references 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.)
Komunjer, Ivana, 2002.
"Quasi-Maximum Likelihood Estimation for Conditional Quantiles ,"
Working Papers
1139, California Institute of Technology, Division of the Humanities and Social Sciences.
[Downloadable!]
Other versions: Cristian Huse, 2004.
"Comparing Nonparametric Regression Quantiles ,"
Econometric Society 2004 Latin American Meetings
165, Econometric Society.
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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.
[Downloadable!] (restricted)
Other versions: Yang Yang & Tae-Hwy Lee, 2004.
"Bagging Binary Predictors for Time Series ,"
Econometric Society 2004 Far Eastern Meetings
512, Econometric Society.
[Downloadable!]
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