- Bayesian Anal.
- Volume 13, Number 4 (2018), 1137-1161.
Sequential Monte Carlo Smoothing with Parameter Estimation
We propose two new sequential Monte Carlo (SMC) smoothing methods for general state-space models with unknown parameters. The first is a modification of the particle learning and smoothing (PLS) algorithm of Carvalho, Johannes, Lopes, and Polson (2010), with an adjustment in the backward resampling weights. The second, called Refiltering, is a two-stage method that combines sequential parameter learning and particle smoothing algorithms. We illustrate the methods on three benchmark models using simulated data, and apply them to a stochastic volatility model for daily S&P 500 index returns during the financial crisis. We show that both new methods outperform existing SMC approaches, and that Refiltering is competitive with smoothing approaches based on Markov chain Monte Carlo (MCMC) and Particle MCMC.
Bayesian Anal., Volume 13, Number 4 (2018), 1137-1161.
First available in Project Euclid: 29 December 2017
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Yang, Biao; Stroud, Jonathan R.; Huerta, Gabriel. Sequential Monte Carlo Smoothing with Parameter Estimation. Bayesian Anal. 13 (2018), no. 4, 1137--1161. doi:10.1214/17-BA1088. https://projecteuclid.org/euclid.ba/1514516432
- Supplementary Material of the Sequential Monte Carlo Smoothing with Parameter Estimation. Supplementary material A provides summaries of the algorithms (MCMC, Particle Filter and PMMH) referenced in the paper. Supplementary material B provides graphical summaries for different estimation methods for the stochastic volatility model with T=3000.