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Testing for Autocorrelation in Quantile Regression Models

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Author Info

  • Lijuan Huo

    (School of Economics, Yonsei University, Korea, Department of Applied Mathematics, School of Science, Changchun University of Science and Technology, China)

  • Tae-Hwan Kim

    (School of Economics, Yonsei University, Korea)

  • Yunmi Kim

    (Department of Economics, Kookmin University, Korea)

Abstract

Quantile regression (QR) models have been increasingly employed in many applied areas in economics. At the early stage, applications took place usually using cross-section data, but recent development has seen a surge of the use of quantile regression in both time-series and panel datasets. However, how to test for possible autocorrelation, especially in the context of time-series models, has been paid little attention. As a rule of thumb, one might attempt to apply the usual Breusch-Godfrey LM test to the residuals from the baseline quantile regression. In this paper, we demonstrate by Monte Carlo simulations that such an application of the LM test can result in potentially large size distortions, especially in either low or high quantiles. We then propose two correct tests (named the F-test and the QR-LM test) for autocorrelation in quantile models, which do not suffer from any size distortion. Monte Carlo simulation demonstrate that the two tests perform fairly well in finite samples, across either different quantiles or different underlying error distributions.

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Bibliographic Info

Paper provided by Yonsei University, Yonsei Economics Research Institute in its series Working papers with number 2013rwp-54.

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Length: 22 pages
Date of creation: 13 Feb 2013
Date of revision:
Handle: RePEc:yon:wpaper:2013rwp-54

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Keywords: Quantile regression; autocorrelation; LM test;

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