Particle efficient importance sampling

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• 作者：Marcel Scharth^a ; Robert Kohn^b ; ^{r.kohn@unsw.edu.au" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Bayesian inference ; Particle filters ; Particle marginal Metropolis&ndash ; Hastings ; Sequential Monte Carlo ; Stochastic volatility
• 刊名：Journal of Econometrics
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：190
• 期：1
• 页码：133-147
• 全文大小：900 K

文摘

The efficient importance sampling (EIS) method is a general principle for the numerical evaluation of high-dimensional integrals that uses the sequential structure of target integrands to build variance minimising importance samplers. Despite a number of successful applications in high dimensions, it is well known that importance sampling strategies are subject to an exponential growth in variance as the dimension of the integration increases. We solve this problem by recognising that the EIS framework has an offline sequential Monte Carlo interpretation. The particle EIS method is based on non-standard resampling weights that take into account the construction of the importance sampler as a sequential approximation to the state smoothing density. We apply the method for a range of univariate and bivariate stochastic volatility specifications. We also develop a new application of the EIS approach to state space models with Student’s bdece548ee" title="Click to view the MathML source">t$t$ state innovations. Our results show that the particle EIS method strongly outperforms both the standard EIS method and particle filters for likelihood evaluation in high dimensions. We illustrate the efficiency of the method for Bayesian inference using the particle marginal Metropolis–Hastings and importance sampling squared algorithms.