Erschienen:
Cambridge, Mass: National Bureau of Economic Research, May 2017
Erschienen in:NBER working paper series ; no. w23448
Umfang:
1 Online-Ressource
Sprache:
Englisch
DOI:
10.3386/w23448
Identifikator:
Reproduktionsnotiz:
Hardcopy version available to institutional subscribers
Entstehung:
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Mode of access: World Wide Web
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Beschreibung:
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