Journal article

Variational Bayes Estimation of Discrete-Margined Copula Models With Application to Time Series

Ruben Loaiza-Maya, Michael Stanley Smith

Journal of Computational and Graphical Statistics | Taylor & Francis | Published : 2019

Abstract

We propose a new variational Bayes (VB) estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational approximation to a tractable augmented posterior and is faster than previous likelihood-based approaches. We use it to estimate drawable vine copulas for univariate and multivariate Markov ordinal and mixed time series. These have dimension rT, where T is the number of observations and r is the number of series, and are difficult to estimate using previous methods. The vine pair-copulas are carefully selected to allow for heteroscedasticity, which is a feature of most ordinal time series data. When combined wi..

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University of Melbourne Researchers