Volume 59, 2017Thematic Cycle on Monte-Carlo techniques
|Page(s)||76 - 103|
|Published online||08 November 2017|
Some recent developments in Markov Chain Monte Carlo for cointegrated time series
1 Imperial College London, UK
2 University College London, UK
3 Imperial College London, UK
3 ESC Rennes School of Business, France
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  and the Geodesic Hamiltonian Monte Carlo method of . 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.
© EDP Sciences, SMAI 2017
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