Three-stage estimation method for non-linear multiple time-series - HAL-SHS - Sciences de l'Homme et de la Société Accéder directement au contenu
Autre Publication Scientifique Année : 2017

Three-stage estimation method for non-linear multiple time-series

Dominique Guegan
Giovanni de Luca
  • Fonction : Auteur
  • PersonId : 998790
Giorgia Rivieccio
  • Fonction : Auteur
  • PersonId : 998791

Résumé

We present the three-stage pseudo maximum likelihood estimation in order to reduce the computational burdens when a copula-based model is applied to multiple time-series in high dimensions. The method is applied to general stationary Markov time series, under some assumptions which include a time-invariant copula as well as marginal distributions, extending the results of Yi and Liao [2010]. We explore, via simulated and real data, the performance of the model compared to the classical vectorial autoregressive model, giving the implications of misspecified assumptions for margins and/or joint distribution and providing tail dependence measures of economic variables involved in the analysis.
Fichier principal
Vignette du fichier
17001.pdf (1.04 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

halshs-01439860 , version 1 (18-01-2017)

Identifiants

  • HAL Id : halshs-01439860 , version 1

Citer

Dominique Guegan, Giovanni de Luca, Giorgia Rivieccio. Three-stage estimation method for non-linear multiple time-series. 2017. ⟨halshs-01439860⟩
341 Consultations
196 Téléchargements

Partager

Gmail Facebook X LinkedIn More