Tempered Particle Filtering
Edward Herbst () and
Frank Schorfheide ()
No 2016-072, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (US)
The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t-1 particle values into time t values. In the widely-used bootstrap particle filter this distribution is generated by the state-transition equation. While straightforward to implement, the practical performance is often poor. We develop a self-tuning particle filter in which the proposal distribution is constructed adaptively through a sequence of Monte Carlo steps. Intuitively, we start from a measurement error distribution with an inflated variance, and then gradually reduce the variance to its nominal level in a sequence of steps that we call tempering. We show that the filter generates an unbiased and consistent approximation of the likelihood function. Holding the run time fixed, our filter is substantially more accurate in two DSGE model applications than the bootstrap particle filter.
Keywords: Bayesian Analysis; DSGE Models; Monte Carlo Methods; Nonlinear Filtering (search for similar items in EconPapers)
JEL-codes: C11 C15 E10 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-sog
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Journal Article: Tempered particle filtering (2019)
Working Paper: Tempered Particle Filtering (2017)
Working Paper: Tempered Particle Filtering (2016)
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