Count Time Series Modeling with Gaussian Copulas
Year: 2018
Type: preprint
Abstract: This paper develops the theory and methods for copula modeling of a stationary count time series. The techniques use a latent Gaussian process and a distributional transformation to construct stationary series with very flexible correlation features that can have any pre-specified marginal distribution, including the classical Poisson, generalized Poisson, negative binomial, and binomial structures. A Gaussian pseudo-likelihood estimation paradigm, based only on the mean and autocovariance function of the count series, is developed via a new Hermite expansion. Particle filtering methods are used to conduct likelihood estimation. Connections to hidden Markov models are made. The performance of the estimation approaches are evaluated in a simulation study and the methods are used to analyze a count series of weekly retail sales.
Source: arXiv (Cornell University)
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3092898978