Computational methods for a copula-based Markov chain model with a binomial time series

A copula-based Markov chain model can flexibly capture serial dependence in a time series. However, the computational developments for models remain insufficient discrete marginal compared with continuous ones. In this article, we develop methods binomial series under Clayton and Joe copulas. The include data-generation, parameter estimation, selection, goodness-of-fit tests. We implement our R package Copula.Markov (https://CRAN.R-project.org/package=Copula.Markov). conduct simulations to see performance of developed methods. Finally, proposed method is illustrated by real dataset.