| Issue |
ESAIM: ProcS
Volume 59, 2017
Thematic Cycle on Monte-Carlo techniques
|
|
|---|---|---|
| Page(s) | 76 - 103 | |
| DOI | https://doi.org/10.1051/proc/201759076 | |
| 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 [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.
© EDP Sciences, SMAI 2017
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