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A1513
Topic: Contributed on Sequential Monte Carlo methods in econometrics Title: Data driven particle filters for particle Markov chain Monte Carlo Authors:  Patrick Leung - Monash University (Australia) [presenting]
Catherine Forbes - Monash University (Australia)
Gael Martin - Monash University (Australia)
Abstract: New automated proposal distributions are proposed for sequential Monte Carlo (SMC) algorithms, including particle filtering and related sequential importance sampling methods. The weights for these proposal distributions are easily established, as is the unbiasedness property of the resultant likelihood estimators, so that the methods may be used within a particle Markov chain Monte Carlo (PMCMC) inferential setting. Simulation exercises, based on a range of important financial models, are used to demonstrate the linkage between the signal to noise ratio of the system and the performance of the new particle filters, in comparison with existing filters. In particular, we demonstrate that one of our proposed filters performs well in a high signal-to-noise ratio setting, that is, when the observation is informative in identifying the location of the unobserved state. A second filter, deliberately designed to draw proposals that are informed by both the current observation and past states, is shown to work well across a range of signal-to-noise ratios. We then extend the study to a PMCMC setting in which we document the performance of the PMCMC algorithm using the new filters to estimate the likelihood function, once again in comparison with existing alternatives. The comparison is based on the optimal computing time required to estimate the posterior distribution of the parameter of interest.