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A Generalized Fast Algorithm for BDS-Type Statistics

Author

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  • Mayer-Foulkes David

    (Centro de Investigación y Docencia Económicas)

Abstract

We provide a fast algorithm to calculate the m-dimensional distance histogram on which Brock, Dechert, and Sheinkman's (1987) BDS-type statistics are based. The algorithm generalizes a fast algorithm due to LeBaron by calculating the histogram for any finite set of distances simultaneously, and also using induction in m. By reordering the calculation appropriately, the algorithm also requires less memory and time. The two algorithms are compared using LeBaron's MS-DOS implementation in C and our Delphi (Windows Pascal) program. The generalized algorithm is faster when more than a few values of m and M (the distance parameter) are required, and is set up to calculate up to 255 values using short-integer arithmetic.

Suggested Citation

  • Mayer-Foulkes David, 2000. "A Generalized Fast Algorithm for BDS-Type Statistics," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 4(1), pages 1-7, April.
    • Handle: RePEc:bpj:sndecm:v:4:y:2000:i:1:n:al2
    DOI: 10.2202/1558-3708.1055
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    References listed on IDEAS

    as
    1. Mayer-Foulkes, David, 1995. "A statistical correlation dimension," Journal of Empirical Finance, Elsevier, vol. 2(3), pages 277-293, September.
    2. Brock, W. A., 1986. "Distinguishing random and deterministic systems: Abridged version," Journal of Economic Theory, Elsevier, vol. 40(1), pages 168-195, October.
    3. LeBaron Blake, 1997. "A Fast Algorithm for the BDS Statistic," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(2), pages 1-9, July.
    Full references (including those not matched with items on IDEAS)

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