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Copulas and Temporal Dependence

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  • Beare, Brendan K.
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    Abstract

    An emerging literature in time series econometrics concerns the modeling of potentially nonlinear temporal dependence in stationary Markov chains using copula functions. We obtain sucient conditions for a geometric rate of mixing in models of this kind. Geometric beta-mixing is established under a rather strong sucient condition that rules out asymmetry and tail dependence in the copula function. Geometric rho-mixing is obtained under a weaker condition that permits both asymmetry and tail dependence. We verify one or both of these conditions for a range of parametric copula functions that are popular in applied work.

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    Bibliographic Info

    Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number qt87p829d4.

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    Date of creation: 23 Mar 2009
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    Handle: RePEc:cdl:ucsdec:qt87p829d4

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    Related research

    Keywords: copula; Markov chain; maximal correlation; mean square contingency; mixing; canonical correlatin; tail dependence;

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    1. P. Gagliardini & C. Gourieroux, 2008. "Duration time-series models with proportional hazard," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(1), pages 74-124, 01.
    2. McCAUSLAND, William J., 2004. "Time Reversibility of Stationary Regular Finite State Markov Chains," Cahiers de recherche 09-2004, Centre interuniversitaire de recherche en ├ęconomie quantitative, CIREQ.
    3. Brendan K. Beare, 2007. "A New Mixing Condition," Economics Series Working Papers 348, University of Oxford, Department of Economics.
    4. Chen, Xiaohong & Hansen, Lars Peter & Carrasco, Marine, 2008. "Nonlinearity and Temporal Dependence," Working Papers 48, Yale University, Department of Economics.
    5. Xiaohong Chen & Wei Biao Wu & Yanping Yi, 2009. "Efficient Estimation of Copula-based Semiparametric Markov Models," Cowles Foundation Discussion Papers 1691, Cowles Foundation for Research in Economics, Yale University, revised Mar 2009.
    6. Lorraine Dearden & Emla Fitzsimons & Alissa Goodman & Greg Kaplan, 2008. "Higher Education Funding Reforms in England: The Distributional Effects and the Shifting Balance of Costs," Economic Journal, Royal Economic Society, vol. 118(526), pages F100-F125, 02.
    7. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation of copula-based semiparametric time series models," Journal of Econometrics, Elsevier, vol. 130(2), pages 307-335, February.
    8. Arcones, Miguel A., 1995. "On the central limit theorem for U-statistics under absolute regularity," Statistics & Probability Letters, Elsevier, vol. 24(3), pages 245-249, August.
    9. Gagliardini, Patrick & Gourieroux, Christian, 2007. "An efficient nonparametric estimator for models with nonlinear dependence," Journal of Econometrics, Elsevier, vol. 137(1), pages 189-229, March.
    10. Rustam Ibragimov, 2005. "Copula-Based Dependence Characterizations and Modeling for Time Series," Harvard Institute of Economic Research Working Papers 2094, Harvard - Institute of Economic Research.
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