"Particle Learning and Smoothing"

Michael Johannes, Carlos Carvalho, Hedibert Lopes, Nicholas Polson

© Statistical Science, 2010
Volume: 25 | Issue: 1 | Pages: 88-106

Publication type: Journal article

Research Archive Topic: Business Economics and Public Policy, Corporate Finance

Abstract

Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State smoothing in the presence of parameter uncertainty is also solved as a by-product of PL. In a number of examples, we show that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.

Download PDF

 

Each author name for a Columbia Business School faculty member is linked to a faculty research page, which lists additional publications by that faculty member.

Each topic is linked to an index of publications on that topic.