Hierarchical Markov-Switching Models for Multivariate Integer-valued Time-series
29 Pages Posted: 16 Dec 2017 Last revised: 5 Dec 2019
Date Written: December 13, 2017
Abstract
We propose a new flexible dynamic model for multivariate nonnegative integer–valued time–series. Observations are assumed to depend on the realization of two unobserved integer–valued stochastic variables which control for the time– and cross–dependence of the data. We provide conditional and unconditional (cross)–moments implied by the model, as well as the limiting distribution of the series. An Expectation–Maximization algorithm for maximum likelihood estimation of the model’s parameters is derived. A Monte Carlo experiment investigates the finite sample properties of our estimation methodology. Constrained specifications of the model are recovered by modifying the assumptions about the dependence structure of the latent variables and model identification is discussed accordingly. An application by means of a crime data set from the New South Wales (NSW) Bureau Of Crime Statistics And Research with observations spanning beyond 20 years is reported. Results indicate that the proposed approach provides a good description of the conditional distribution of crime records, outperforming the standard hidden Markov model
Keywords: Hidden Markov Model, Mixture Model, Hierarchical Model, NSW Crime Data
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