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Rank-based entropy tests for serial independence

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Author Info
Diks, C.G.H. () (Universiteit van Amsterdam)
Panchenko, V. () (University of New South Wales)

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Abstract

In nonparametric tests for serial independence the marginal distribution of the data acts as an infinite dimensional nuisance parameter. The decomposition of joint distributions in terms of a copula density and marginal densities shows that in general empirical marginals carry no information on dependence. It follows that the order of ranks is sufficient for inference, which motivates transforming the data to a pre-specified marginal distribution prior to testing. As a test statistic we use an estimator of the marginal redundancy, which has some desirable properties in the case of transforming to uniform marginals. We numerically study the finite sample properties of these tests when the data are transformed to uniform as well as normal marginals. The performance of the tests is compared with that of the BDS test as well as with a parametric rank-based test against ARCH alternatives.

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Publisher Info
Paper provided by Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance in its series CeNDEF Working Papers with number 06-14.

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Date of creation: 2006
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Handle: RePEc:ams:ndfwpp:06-14

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Postal: Dept. of Economics and Econometrics, Universiteit van Amsterdam, Roetersstraat 11, NL - 1018 WB Amsterdam, The Netherlands
Phone: + 31 20 525 52 58
Fax: + 31 20 525 52 83
Web page: http://www.fee.uva.nl/cendef/
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  1. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669. [Downloadable!] (restricted)
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  2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April. [Downloadable!] (restricted)
  3. W. A. Broock & J. A. Scheinkman & W. D. Dechert & B. LeBaron, 1996. "A test for independence based on the correlation dimension," Econometric Reviews, Taylor and Francis Journals, vol. 15(3), pages 197-235. [Downloadable!] (restricted)
  4. C. W. Granger & E. Maasoumi & J. Racine, 2004. "A Dependence Metric for Possibly Nonlinear Processes," Journal of Time Series Analysis, Blackwell Publishing, vol. 25(5), pages 649-669, 09. [Downloadable!] (restricted)
  5. Yongmiao Hong & Halbert White, 2005. "Asymptotic Distribution Theory for Nonparametric Entropy Measures of Serial Dependence," Econometrica, Econometric Society, vol. 73(3), pages 837-901, 05. [Downloadable!] (restricted)
  6. Cees Diks & Sebastiano Manzan, 2002. "Tests for Serial Independence and Linearity Based on Correlation Integrals," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 6(2), pages 1005-1005. [Downloadable!] (restricted)
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