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Full-Text Articles in Longitudinal Data Analysis and Time Series
Copula Modelling Of Dependence In Multivariate Time Series, Michael S. Smith
Copula Modelling Of Dependence In Multivariate Time Series, Michael S. Smith
Michael Stanley Smith
Almost all existing nonlinear multivariate time series models remain linear, conditional on a point in time or latent regime. Here, an alternative is proposed, where nonlinear serial and cross-sectional dependence is captured by a copula model. The copula defines a multivariate time series on the unit cube. A drawable vine copula is employed, along with a factorization which allows the marginal and transitional densities of the time series to be expressed analytically. The factorization also provides for simple conditions under which the series is stationary and/or Markov, as well as being parsimonious. A parallel algorithm for computing the likelihood is …