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

Dominique Guegan (), Giovanni De Luca () and Giorgia Rivieccio ()
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Dominique Guegan: Centre d'Economie de la Sorbonne, https://cv.archives-ouvertes.fr/dominique-guegan
Giovanni De Luca: Parthenope University of Naples
Giorgia Rivieccio: Parthenope University of Naples

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

Abstract: 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

Keywords: Copula function; Three stage estimator; Multiple time series (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2017-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:17001

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