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Goodness-Of-Fit Tests For Multivariate Copula-Based Time Series Models

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  • Berghaus, Betina
  • Bücher, Axel

Abstract

In recent years, stationary time series models based on copula functions became increasingly popular in econometrics to model nonlinear temporal and cross-sectional dependencies. Within these models, we consider the problem of testing the goodness-of-fit of the parametric form of the underlying copula. Our approach is based on a dependent multiplier bootstrap and it can be applied to any stationary, strongly mixing time series. The method extends recent i.i.d. results by Kojadinovic et al. (2011) and shares the same computational benefits compared to methods based on a parametric bootstrap. The finite-sample performance of our approach is investigated by Monte Carlo experiments for the case of copula-based Markovian time series models.

Suggested Citation

  • Berghaus, Betina & Bücher, Axel, 2017. "Goodness-Of-Fit Tests For Multivariate Copula-Based Time Series Models," Econometric Theory, Cambridge University Press, vol. 33(2), pages 292-330, April.
    • Handle: RePEc:cup:etheor:v:33:y:2017:i:02:p:292-330_00
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    Cited by:

    1. Kojadinovic, Ivan, 2017. "Some copula inference procedures adapted to the presence of ties," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 24-41.
    2. Bücher Axel & Jaser Miriam & Min Aleksey, 2021. "Detecting departures from meta-ellipticity for multivariate stationary time series," Dependence Modeling, De Gruyter, vol. 9(1), pages 121-140, January.
    3. Axel Bücher & Ivan Kojadinovic, 2019. "A Note on Conditional Versus Joint Unconditional Weak Convergence in Bootstrap Consistency Results," Journal of Theoretical Probability, Springer, vol. 32(3), pages 1145-1165, September.

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