Testing for Autocorrelation in Quantile Regression Models
AbstractQuantile 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Yonsei University, Yonsei Economics Research Institute in its series Working papers with number 2013rwp-54.
Length: 22 pages
Date of creation: 13 Feb 2013
Date of revision:
Quantile regression; autocorrelation; LM test;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-03-09 (All new papers)
- NEP-ECM-2013-03-09 (Econometrics)
- NEP-ETS-2013-03-09 (Econometric Time Series)
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.:
- repec:ecu:wpaper:2009-11 is not listed on IDEAS
- Galvao Jr., Antonio F., 2009. "Unit root quantile autoregression testing using covariates," Journal of Econometrics, Elsevier, vol. 152(2), pages 165-178, October.
- Weiss, Andrew A., 1991. "Estimating Nonlinear Dynamic Models Using Least Absolute Error Estimation," Econometric Theory, Cambridge University Press, vol. 7(01), pages 46-68, March.
- Furno, Marilena, 2000. "Lm Tests In The Presence Of Non-Normal Error Distributions," Econometric Theory, Cambridge University Press, vol. 16(02), pages 249-261, April.
- Zhijie Xiao, 2009.
"Quantile Cointegrating Regression,"
Boston College Working Papers in Economics
708, Boston College Department of Economics.
- Koul, H. L. & Mukherjee, K., 1994. "Regression Quantiles and Related Processes Under Long Range Dependent Errors," Journal of Multivariate Analysis, Elsevier, vol. 51(2), pages 318-337, November.
- Abrevaya, Jason & Dahl, Christian M, 2008. "The Effects of Birth Inputs on Birthweight," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 379-397.
- Weiss, Andrew A., 1990. "Least absolute error estimation in the presence of serial correlation," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 127-158.
- Roger Koenker & Zhijie Xiao, 2004. "Unit Root Quantile Autoregression Inference," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 775-787, January.
- Koenker,Roger, 2005.
Cambridge University Press, number 9780521845731, October.
- Breusch, T S, 1978. "Testing for Autocorrelation in Dynamic Linear Models," Australian Economic Papers, Wiley Blackwell, vol. 17(31), pages 334-55, December.
- M. N. Hasan & R. W. Koenker, 1997. "Robust Rank Tests of the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 65(1), pages 133-162, January.
- Antonio F. Galvao Jr. & Gabriel Montes‐Rojas & Jose Olmo, 2011. "Threshold quantile autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 253-267, 05.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- Godfrey, Leslie G, 1978. "Testing for Higher Order Serial Correlation in Regression Equations When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1303-10, November.
- Antonio F. Galvao JR. & Gabriel Montes-Rojas & Sung Y. Park, 2013. "Quantile Autoregressive Distributed Lag Model with an Application to House Price Returns," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 307-321, 04.
- Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
- Fama, Eugene F & French, Kenneth R, 1996. " Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, American Finance Association, vol. 51(1), pages 55-84, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (YERI).
If references are entirely missing, you can add them using this form.