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Activity Number: 414
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303483
Title: Particle Learning in Autoregressive Models with Structured Priors
Author(s): Raquel Prado*+ and Hedibert F. Lopes
Companies: University of California, Santa Cruz and The University of Chicago
Address: Baskin School of Engineering MS:SOE2, UCSC, Santa Cruz, CA, 95064,
Keywords: autoregressions ; state-space models ; structured priors ; particle learning ; sequential Monte Carlo

We consider sequential parameter learning and filtering in state-space models where the states or the observations follow an autoregressive process with structured priors. Following an approach similar to that presented in Huerta and West (1999), priors are built on the reciprocal roots of the AR characteristic polynomial. Models with structured priors on the reciprocal roots appropriately incorporate scientifically meaningful information in applied scenarios. However, standard methods for posterior inference based on MCMC are not computationally efficient in settings where online estimation and filtering are required. We propose an algorithm based on the particle learning approach of Carvalho, Johannes, Lopes and Polson (2008). We compare the performance of such algorithm to alternative particle methods in simulated and real data sets.

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