Working Papers
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Paper #  Author  Title  

16017 
Edward Herbst Frank Schorfheide 
Tempered Particle Filtering  
The accuracy of particle filters for nonlinear statespace models crucially depends on the proposal distribution that mutates time t − 1 particle values into time t values. In the widelyused 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 selftuning 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. Download Paper
