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A Sequential Particle Filter Method for Static Models

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  • Nicolas Chopin

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Abstract

Particle filter methods are complex inference procedures, which combine importance sampling and Monte Carlo schemes in order to explore consistently a sequence of multiple distributions of interest. We show that such methods can also offer an efficient estimation tool in 'static' set-ups, in which case &pgr;(&thgr; | y-sub-1, …, y-sub-N) (n
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  • Nicolas Chopin, 2000. "A Sequential Particle Filter Method for Static Models," Working Papers 2000-45, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2000-45
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