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Testing For Zero Autocorrelation In The Presence Of Statistical Dependence

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Author Info
Lobato, I.N.
Nankervis, John C.
Savin, N.E.

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Abstract

The problem addressed in this paper is to test the null hypothesis that a time series process is uncorrelated up to lag K in the presence of statistical dependence. We propose an extension of the Box Pierce Q-test that is asymptotically distributed as chi-square when the null is true for a very general class of dependent processes that includes non-martingale difference sequences. The test is based on a consistent estimator of the asymptotic covariance matrix of the sample autocorrelations under the null. The finite sample performance of this extension is investigated in a Monte Carlo study.

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File URL: http://journals.cambridge.org/abstract_S0266466602183083
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Publisher Info
Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 18 (2002)
Issue (Month): 03 (June)
Pages: 730-743
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Handle: RePEc:cup:etheor:v:18:y:2002:i:03:p:730-743_18

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  1. Carlos Velasco & Ignacio N. Lobato, 2004. "A simple and general test for white noise," Econometric Society 2004 Latin American Meetings 112, Econometric Society. [Downloadable!]
  2. Jen-Je Su, 2005. "On the size and power of testing for no autocorrelation under weak assumptions," Applied Financial Economics, Taylor and Francis Journals, vol. 15(4), pages 247-257, February. [Downloadable!] (restricted)
  3. Jen-Je Su, 2004. "Testing for no autocorrelation using a modified Lobato test," Economics Bulletin, Economics Bulletin, vol. 3(46), pages 1-9. [Downloadable!]
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This page was last updated on 2009-12-20.


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