Tempered particle filtering
| Autor: | Frank Schorfheide, Edward P. Herbst |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Economics and Econometrics
Observational error Applied Mathematics 05 social sciences Monte Carlo method Monte Carlo localization 01 natural sciences Nominal level 010104 statistics & probability Filter (video) 0502 economics and business Statistics 050207 economics 0101 mathematics Likelihood function Particle filter Algorithm Auxiliary particle filter Mathematics |
| Zdroj: | Journal of Econometrics. 210:26-44 |
| ISSN: | 0304-4076 |
| DOI: | 10.1016/j.jeconom.2018.11.003 |
| Popis: | 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 tempering steps. 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. |
| Databáze: | OpenAIRE |
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