First-Order Random Coefficient Multinomial Autoregressive Model for Finite-Range Time Series of Counts

Abstract

In view of the complexity and asymmetry of finite range multi-state integer-valued time series data, we propose a first-order random coefficient multinomial autoregressive model in this paper. Basic probabilistic and statistical properties of the model are discussed. Conditional least squares (CLS) and weighted conditional least squares (WCLS) estimators of the model parameters are derived, and their asymptotic properties are established. In simulation studies, we compare these two methods with the conditional maximum likelihood (CML) method to verify the proposed procedure. A real example is applied to illustrate the advantages of our model.

Keywords

• multinomial autoregressive process
• random coefficient
• binomial thinning
• parameter estimation