期刊名称：Documents de Travail du Centre d'Economie de la Sorbonne
印刷版ISSN：1955-611X
出版年度：2017
出版社：Centre d'Economie de la Sorbonne
摘要：We present the three-stage pseudo maximum likelihood estimation in order toreduce the computational burdens when a copula-based model is applied to mul-tiple time-series in high dimensions. The method is applied to general station-ary Markov time series, under some assumptions which include a time-invariantcopula as well as marginal distributions, extending the results of Yi and Liao[2010]. We explore, via simulated and real data, the performance of the modelcompared to the classical vectorial autoregressive model, giving the implications of misspeci ed assumptions for margins and/or joint distribution and providing tail dependence measures of economic variables involved in the analysis.
关键词：Copula function; Three stage estimator; Multiple time series.
