期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 26, 期 4, 页码 905-917出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/10618600.2017.1322091
关键词
Ancillarity-sufficiency interweaving strategy (ASIS); Curse of dimensionality; Data augmentation; Dynamic correlation; Dynamic covariance; Exchange rate data; Markov chain Monte Carlo (MCMC)
We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit nonidentifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate dataset illustrates the superior performance of the new approach for real-world data. Supplementary materials for this article are available online.
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