Mostrando recursos 1 - 2 de 2

  1. Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

    Kastner, Gregor; Frühwirth-Schnatter, Sylvia; Lopes, Hedibert Freitas
    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 (Yu and Meng, Journal of Computational and Graphical Statistics, 20(3), 531-570, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability 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...
    (application/pdf) - 18-oct-2016

  2. Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

    Kastner, Gregor; Frühwirth-Schnatter, Sylvia; Lopes, Hedibert Freitas
    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 (Yu and Meng, Journal of Computational and Graphical Statistics, 20(3), 531-570, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability 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...
    (application/pdf) - 29-ago-2017

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