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GOODNESS-OF-FIT TESTS FOR MULTIVARIATE COPULA-BASED TIME SERIES MODELS

Published online by Cambridge University Press:  29 January 2016

Betina Berghaus  and
Axel Bücher
Betina Berghaus*
Affiliation:
Ruhr-Universität Bochum
Axel Bücher*
Affiliation:
Ruhr-Universität Bochum
*
*Address correspondence to Betina Berghaus, Ruhr-Universität Bochum, Fakultät für Mathematik, Universitätsstr. 150, 44780 Bochum, Germany. E-mail: [email protected], [email protected].
*Address correspondence to Betina Berghaus, Ruhr-Universität Bochum, Fakultät für Mathematik, Universitätsstr. 150, 44780 Bochum, Germany. E-mail: [email protected], [email protected].
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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.

Type
ARTICLES
Information
Econometric Theory , Volume 33 , Issue 2 , April 2017 , pp. 292 - 330
Copyright
Copyright © Cambridge University Press 2016 

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