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Rank-based Entropy Tests for Serial Independence

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  • Diks Cees

    (CeNDEF, University of Amsterdam)

  • Panchenko Valentyn

    (School of Economics, University of New South Wales)

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. We numerically study the finite sample properties of the tests obtained when the data are transformed to uniform as well as normal marginals. For comparison purposes we also derive a rank-based test against local ARCH alternatives. The performance of the new tests is compared with a modified version of the BDS test and with the Ljung-Box test.

Suggested Citation

  • Diks Cees & Panchenko Valentyn, 2008. "Rank-based Entropy Tests for Serial Independence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(1), pages 1-21, March.
    • Handle: RePEc:bpj:sndecm:v:12:y:2008:i:1:n:2
    DOI: 10.2202/1558-3708.1476
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    9. Diks Cees & Manzan Sebastiano, 2002. "Tests for Serial Independence and Linearity Based on Correlation Integrals," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(2), pages 1-22, July.
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    Cited by:

    1. Pál Rakonczai & László Márkus & András Zempléni, 2012. "Autocopulas: Investigating the Interdependence Structure of Stationary Time Series," Methodology and Computing in Applied Probability, Springer, vol. 14(1), pages 149-167, March.
    2. Luca Bagnato & Lucio De Capitani & Antonio Punzo, 2014. "Testing Serial Independence via Density-Based Measures of Divergence," Methodology and Computing in Applied Probability, Springer, vol. 16(3), pages 627-641, September.

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