Some recent developments in Markov Chain Monte Carlo for cointegrated time series

Maciej Marowka, Gareth W. Peters, Nikolas Kantas, Guillaume Bagnarosa

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Abstract

We consider multivariate time series that exhibit reduced rank cointegration, which means a lower dimensional linear projection of the process becomes stationary. We will review recent suitable Markov Chain Monte Carlo approaches for Bayesian inference such as the Gibbs sampler of [41] and the Geodesic Hamiltonian Monte Carlo method of [3]. Then we will propose extensions that can allow the ideas in both methods to be applied for cointegrated time series with non-Gaussian noise. We illustrate the efficiency and accuracy of these extensions using appropriate numerical experiments.
Original languageEnglish
Pages (from-to)76-103
Number of pages28
JournalESAIM: Proceedings and Surveys
Volume59
DOIs
Publication statusPublished - 8 Nov 2017

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