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Copulas and Temporal Dependence

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  • Beare, Brendan
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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 conditions that imply a geometric rate of mixing in models of this kind. A geometric rate of beta-mixing is shown to obtain under a rather strong condition that rules out asymmetry and tail dependence in the copula function. Rho-mixing, which implies a geometric rate of alpha-mixing, is obtained under a much weaker condition. We verify one or both of these conditions for a range of parametric copula functions that are opular in applied work.

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    File URL: http://www.escholarship.org/uc/item/2880q2jq.pdf;origin=repeccitec
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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 qt2880q2jq.

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    Date of creation: 22 Sep 2008
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    Handle: RePEc:cdl:ucsdec:qt2880q2jq

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    Related research

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

    References

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    1. McCausland, William J., 2007. "Time reversibility of stationary regular finite-state Markov chains," Journal of Econometrics, Elsevier, vol. 136(1), pages 303-318, January.
    2. Lorraine Dearden & Emla Fitzsimons & Alissa Goodman & Greg Kaplan, 2007. "Higher education funding reforms in England: the distributional effects and the shifting balance of costs," IFS Working Papers W07/18, Institute for Fiscal Studies.
    3. 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.
    4. Chen, Xiaohong & Hansen, Lars Peter & Carrasco, Marine, 2008. "Nonlinearity and Temporal Dependence," Working Papers 48, Yale University, Department of Economics.
    5. Patrick Gagliardini & Christian Gourieroux, 2002. "Duration Time Series Models with Proportional Hazard," Working Papers 2002-21, Centre de Recherche en Economie et Statistique.
    6. 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.
    7. Brendan K. Beare, 2007. "A New Mixing Condition," Economics Series Working Papers 348, University of Oxford, Department of Economics.
    8. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation of copula-based semiparametric time series models," Journal of Econometrics, Elsevier, vol. 130(2), pages 307-335, February.
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    Cited by:
    1. Xiaohong Chen & Roger Koenker & Zhijie Xiao, 2009. "Copula-based nonlinear quantile autoregression," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages S50-S67, 01.
    2. 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.
    3. Xiaohong Chen & Wei Biao Wu & Yanping Yi, 2009. "Efficient estimation of copula-based semiparametric Markov models," CeMMAP working papers CWP06/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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