Working Paper n°2018-01 – Negative Binomial Autoregressive Process – Yang Lu (CEPN), Christian Gourieroux (University of Toronto and Toulouse School of Economics)

We introduce Negative Binomial Autoregressive (NBAR) processes for (univariate and bivariate) count time series. The univariate NBAR process is defined jointly with an underlying intensity process, which is autoregressive gamma. The resulting count process is Markov, with negative binomial conditional and marginal distributions. The process is then extended to the bivariate case with a Wishart autoregressive matrix intensity process. The NBAR processes are Compound Autoregressive, which allows for simple stationarity condition and quasi-closed form nonlinear forecasting formulas at any horizon, as well as a computationally tractable generalized method of moment estimator. The model is applied to a pairwise analysis of weekly occurrence counts of a contagious disease between the greater Paris region and other French regions.

Keywords: Compound Autoregressive, Poisson-gamma conjugacy

JEL Codes:  C32.

Consulter ce document de travailIdeas

Consulter la liste des documents de travail du CEPN les plus récentsIdeas