Non-linear INAR(1) processes under an alternative geometric thinning operator

We propose a novel class of first-order integer-valued AutoRegressive (INAR(1)) models based on a new operator, the so-called geometric thinning operator, which induces a certain non-linearity to the models. We show that this non-linearity can produce better results in terms of prediction when compared to the linear case commonly considered in the literature. The new models are named non-linear INAR(1) (in short NonLINAR(1)) processes. We explore both stationary and non-stationary versions of the NonLINAR processes. Inference on the model parameters is addressed and the finite-sample behavior of the estimators investigated through Monte Carlo simulations. Two real data sets are analyzed to illustrate the stationary and non-stationary cases and the gain of the non-linearity induced for our method over the existing linear methods. A generalization of the geometric thinning operator and an associated NonLINAR process are also proposed and motivated for dealing with zero-inflated or zero-deflated count time series data.

Automated summary

We propose a novel class of INAR(1) models based on the geometric thinning operator, which introduces non-linearity to the models. The non-linear INAR(1) processes, named NonLINAR(1), are shown to produce better prediction results compared to the linear case. Stationary and non-stationary versions of the NonLINAR processes are explored, and model parameter inference and finite-sample behavior are investigated.