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Smooth Tests of Copula Specifications

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  • Juan Lin
  • Ximing Wu

Abstract

We present a family of smooth tests for the goodness of fit of semiparametric multivariate copula models. The proposed tests are distribution free and can be easily implemented. They are diagnostic and constructive in the sense that when a null distribution is rejected, the test provides useful pointers to alternative copula distributions. We then propose a method of copula density construction, which can be viewed as a multivariate extension of Efron and Tibshirani. We further generalize our methods to the semiparametric copula-based multivariate dynamic models. We report extensive Monte Carlo simulations and three empirical examples to illustrate the effectiveness and usefulness of our method.

Suggested Citation

  • Juan Lin & Ximing Wu, 2015. "Smooth Tests of Copula Specifications," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 128-143, January.
  • Handle: RePEc:taf:jnlbes:v:33:y:2015:i:1:p:128-143
    DOI: 10.1080/07350015.2014.932696
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    References listed on IDEAS

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    6. Kojadinovic, Ivan & Segers, Johan & Yan, Jun, 2011. "Large-sample tests of extreme-value dependence for multivariate copulas," LIDAM Reprints ISBA 2011025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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    Cited by:

    1. Bingduo Yang & Zongwu Cai & Christian M. Hafner & Guannan Liu, 2018. "Trending Mixture Copula Models with Copula Selection," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201809, University of Kansas, Department of Economics, revised Sep 2018.
    2. Wojciech CHAREMZA & Carlos DÍAZ & Svetlana MAKAROVA, 2019. "Conditional Term Structure of Inflation Forecast Uncertainty: The Copula Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 5-18, March.

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