Geodesic
Some recent developments in Markov Chain Monte Carlo for cointegrated time series
Maciej Marowka1 ; Gareth W. Peters2 ; Nikolas Kantas1 ; Guillaume Bagnarosa3
1 Imperial College London, UK
2 University College London, UK
3 ESC Rennes School of Business, France
ESAIM. Proceedings, Tome 59 (2017), pp. 76-103.

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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.
DOI : 10.1051/proc/201759076

Maciej Marowka 1 ; Gareth W. Peters 2 ; Nikolas Kantas 1 ; Guillaume Bagnarosa 3

1 Imperial College London, UK
2 University College London, UK
3 ESC Rennes School of Business, France 
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Maciej Marowka; Gareth W. Peters; Nikolas Kantas; Guillaume Bagnarosa. Some recent developments in Markov Chain Monte Carlo for cointegrated time series. ESAIM. Proceedings, Tome 59 (2017), pp. 76-103. doi : 10.1051/proc/201759076. http://geodesic.mathdoc.fr/articles/10.1051/proc/201759076/

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