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A simple and general test for white noise

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Author Info
Carlos Velasco
Ignacio N. Lobato

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Abstract

This article considers testing that a time series is uncorrelated when it possibly exhibits some form of dependence. Contrary to the currently employed tests that require selecting arbitrary user-chosen numbers to compute the associated tests statistics, we consider a test statistic that is very simple to use because it does not require any user chosen number and because its asymptotic null distribution is standard under general weak dependent conditions, and hence, asymptotic critical values are readily available. We consider the case of testing that the raw data is white noise, and also consider the case of applying the test to the residuals of an ARMA model. Finally, we also study finite sample performance

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Paper provided by Econometric Society in its series Econometric Society 2004 Latin American Meetings with number 112.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:latm04:112

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Related research
Keywords: autocorrelation; spectral analysis; nonlinear dependence;

Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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  6. Franke,J. & Haerdle,W., 1987. "On bootstrapping Kernel spectral estimates," Discussion Paper Serie A 121, University of Bonn, Germany.
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  7. Weiss, Andrew A, 1986. "ARCH and Bilinear Time Series Models: Comparison and Combination," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 59-70, January.
  8. Lobato, I.N. & Nankervis, John C. & Savin, N.E., 2002. "Testing For Zero Autocorrelation In The Presence Of Statistical Dependence," Econometric Theory, Cambridge University Press, vol. 18(03), pages 730-743, June. [Downloadable!]
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  12. He, Changli & Terasvirta, Timo, 1999. "Properties of moments of a family of GARCH processes," Journal of Econometrics, Elsevier, vol. 92(1), pages 173-192, September. [Downloadable!] (restricted)
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