Goodness-of-fit tests for multivariate copula-based time series models

• Verfasst von: Berghaus, Betina [VerfasserIn]
• Verfasst von: Bücher, Axel [VerfasserIn]
• Titel: Goodness-of-fit tests for multivariate copula-based time series models
• Verf.angabe: Betina Berghaus and Axel Bücher
• Jahr: 2017
• Umfang: 39 S.
• Teil: volume:33
• Teil: year:2017
• Teil: number:2
• Teil: pages:292-330
• Teil: extent:39
• Fussnoten: Published online by Cambridge University Press: 29 January 2016 ; Gesehen am 27.09.2021
• Titel Quelle: Enthalten in: Econometric theory
• Ort Quelle: Cambridge : Cambridge Univ. Press, 1985
• Jahr Quelle: 2017
• Band/Heft Quelle: 33(2017), 2, Seite 292-330
• ISSN Quelle: 1469-4360
• DOI: doi:10.1017/S0266466615000419
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe: Berghaus, Betina, 1987 - : Goodness-of-fit tests for multivariate copula-based time series models. - 2017
• K10plus-PPN: 1771827432

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.