Particle Learning and Smoothing

Statistical Science, Vol. 25, No. 1, pp. 88-106, 2010

19 Pages Posted: 22 Oct 2011 Last revised: 9 Nov 2011

Carlos M. Carvalho

University of Texas at Austin - Red McCombs School of Business

Michael S. Johannes

Columbia Business School - Finance and Economics

Hedibert F. Lopes

University of Chicago - Booth School of Business

Nick Polson

University of Chicago - Booth School of Business

Date Written: 2010

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.

Suggested Citation

Carvalho, Carlos M. and Johannes, Michael S. and Lopes, Hedibert F. and Polson, Nick, Particle Learning and Smoothing (2010). Statistical Science, Vol. 25, No. 1, pp. 88-106, 2010 . Available at SSRN: https://ssrn.com/abstract=1947050

Carlos M. Carvalho

University of Texas at Austin - Red McCombs School of Business ( email )

Austin, TX 78712
United States

Michael Slater Johannes

Columbia Business School - Finance and Economics ( email )

3022 Broadway
New York, NY 10027
United States

Hedibert F. Lopes (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

Nick Polson

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
773-702-7513 (Phone)
773-702-0458 (Fax)

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