A multivariate heavy-tailed integer-valued GARCH process with EM algorithm-based inference

TL;DR

This paper introduces a multivariate heavy-tailed count time series model based on a Poisson inverse Gaussian distribution, with novel EM algorithm variants for efficient parameter estimation in high-dimensional settings.

Contribution

It proposes a new multivariate integer-valued GARCH process and develops two EM algorithm variants to address computational challenges in parameter estimation.

Findings

EM algorithms improve estimation efficiency in high dimensions

Model effectively captures heavy tails and autocorrelations in count data

Simulation studies demonstrate robustness and accuracy of the proposed methods

Abstract

A new multivariate integer-valued Generalized AutoRegressive Conditional Heteroscedastic process based on a multivariate Poisson generalized inverse Gaussian distribution is proposed. The estimation of parameters of the proposed multivariate heavy-tailed count time series model via maximum likelihood method is challenging since the likelihood function involves a Bessel function that depends on the multivariate counts and its dimension. As a consequence, numerical instability is often experienced in optimization procedures. To overcome this computational problem, two feasible variants of the Expectation-Maximization (EM) algorithm are proposed for estimating parameters of our model under low and high-dimensional settings. These EM algorithm variants provide computational benefits and help avoid the difficult direct optimization of the likelihood function from the proposed model. Our model and proposed estimation procedures can handle multiple features such as modeling of multivariate counts, heavy-taildness, overdispersion, accommodation of outliers, allowances for both positive and negative autocorrelations, estimation of cross/contemporaneous-correlation, and the efficient estimation of parameters from both statistical and computational points of view. Extensive Monte Carlo simulation studies are presented to assess the performance of the proposed EM algorithms. An application to modeling bivariate count time series data on cannabis possession-related offenses in Australia is discussed.