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Activity Number: 43
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #319569
Title: A Class of Bayesian Multivariate Time Series Models for Counts
Author(s): Refik Soyer* and Tevfik Aktekin and Nicholas Polson
Companies: The George Washington University and University of New Hampshire and The University of Chicago
Keywords: Multivariate counts ; Particle learning ; Particle filtering ; NonGaussian time series

We consider modeling of multivariate time-series of correlated counts which often arise in finance, operations and marketing applications. Dependence among series arises as a result of sharing a common environment. We consider a class of multivariate Poisson time series models by assuming a common environmental process modulating the rates of the individual series. This setup gives us a class of dynamic multivariate negative binomial time series. We develop Bayesian inference for these models using particle filtering and Markov chain Monte Carlo methods. A by-product of particle filtering in our set up is predictive likelihoods which we refer to as multivariate confluent hyper-geometric negative binomial distribution. We discuss issues of sequential filtering, smoothing and prediction and illustrate the proposed models using a simulated data set as well as actual data on weekly household shopping trips.

Authors who are presenting talks have a * after their name.

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