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Nonparametric Tests For Serial Dependence

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  • Ngai Hang Chan
  • Lanh Tat Tran

Abstract

. A nonparametric test statistic based on the distance between the joint and marginal densities is developed to test for the serial dependence for a given sequence of time series data. The key idea lies in observing that, under the null hypothesis of independence, the joint density of the observations is equal to the product of their individual marginals. Histograms are used in constructing such a statistic which is nonparametric and consistent. It possesses high power in capturing subtle or diffuse dependence structure. A bilinear time series model is used to illustrate its performance with the classical correlation approach.

Suggested Citation

  • Ngai Hang Chan & Lanh Tat Tran, 1992. "Nonparametric Tests For Serial Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(1), pages 19-28, January.
    • Handle: RePEc:bla:jtsera:v:13:y:1992:i:1:p:19-28
    DOI: 10.1111/j.1467-9892.1992.tb00092.x
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    Cited by:

    1. Fumiya Akashi & Hiroaki Odashima & Masanobu Taniguchi & Anna Clara Monti, 2018. "A New Look at Portmanteau Tests," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 121-137, February.
    2. Du, Zaichao, 2014. "Testing for serial independence of panel errors," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 248-261.
    3. Hong, Yongmiao, 1996. "Testing for independence between two covariance stationary time series," MPRA Paper 108731, University Library of Munich, Germany.
    4. 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.
    5. C. W. Granger & E. Maasoumi & J. Racine, 2004. "A Dependence Metric for Possibly Nonlinear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 649-669, September.
    6. 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.
    7. Hallin, Marc & Lu, Zudi & Tran, Lanh T., 2004. "Kernel density estimation for spatial processes: the L1 theory," Journal of Multivariate Analysis, Elsevier, vol. 88(1), pages 61-75, January.
    8. Pinkse, Joris, 1998. "A consistent nonparametric test for serial independence," Journal of Econometrics, Elsevier, vol. 84(2), pages 205-231, June.
    9. Miguel A. Delgado, 1996. "Testing Serial Independence Using The Sample Distribution Function," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(3), pages 271-285, May.
    10. Henry Lam, 2018. "Sensitivity to Serial Dependency of Input Processes: A Robust Approach," Management Science, INFORMS, vol. 64(3), pages 1311-1327, March.
    11. Cees Diks & Sebastiano Manzan, 2001. "Tests for Serial Independence and Linearity based on Correlation Integrals," Tinbergen Institute Discussion Papers 01-085/1, Tinbergen Institute.

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