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

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

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