BINMA(1) model with COM-Poisson innovations: Estimation and application

Abstract

The article introduces a first-order bivariate integer-valued moving average process (BINMA(1)) where the respective innovation series are marginally COM-Poisson distributed under nonstationary moments. The purpose of this process is to model inter-related INMA(1) time series that are known to exhibit different levels and types of dispersion. The unknown parameters of the model are estimated by the Generalized Quasi-likelihood (GQL) approach. Simulation experiments and a real-life data application to intra-day stock series of two banking companies is presented.

Keywords:

• BINMA(1)
• Non-Stationary
• CMP
• GQL
• Dispersion

Mathematical Subject Classification:

• 65C60
• 62J12
• 62H12