Tempered Particle Filtering / Edward Herbst, Frank Schorfheide.

Format:

Book

Author/Creator:

Herbst, Edward.

Contributor:

National Bureau of Economic Research.

Schorfheide, Frank.

Series:

Working Paper Series (National Bureau of Economic Research) no. w23448.

NBER working paper series no. w23448

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2017.

Summary:

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.

Notes:

Print version record

May 2017.