A0582
Title: Copula-based bivariate Poisson time series models
Authors: Norou Diawara - Old Dominion University (United States) [presenting]
Abstract: The class of bivariate integer-valued time series models is gaining rapid popularity. However, its efficiency and adaptability are being challenged because of algorithm techniques. The computation will be proposed via copula theory. Each series follows a Markov chain with the serial dependence is captured using copula-based transition probabilities with Poisson and zero-inflated Poisson margins. The copula theory is also used to capture the dependence between the two series using either the bivariate gaussian or $t$ copula functions. Likelihood-based inference is used to estimate the models' parameters with the bivariate integrals of the gaussian or $t$ copula functions being evaluated using standard randomized Monte Carlo methods.
