Integer-valued autoregressive process with flexible marginal and innovation distributions

TL;DR

This paper introduces a flexible INAR model that specifies marginal and innovation distributions directly, overcoming limitations of traditional models, and demonstrates improved fit and forecasting on real count data.

Contribution

It proposes a new INAR model with pre-specified marginals, providing a more flexible and interpretable approach compared to classical models.

Findings

Model has MA(∞) representation and is time-reversible.

Closed-form transition probabilities enable coherent forecasting.

Outperforms existing INAR and INGARCH models on skin lesion data.

Abstract

INteger Auto-Regressive (INAR) processes are usually defined by specifying the innovations and the operator, which often leads to difficulties in deriving marginal properties of the process. In many practical situations, a major modeling limitation is that it is difficult to justify the choice of the operator. To overcome these drawbacks, we propose a new flexible approach to build an INAR model: we pre-specify the marginal and innovation distributions. Hence, the operator is a consequence of specifying the desired marginal and innovation distributions. Our new INAR model has both marginal and innovations geometric distributed, being a direct alternative to the classical Poisson INAR model. Our proposed process has interesting stochastic properties such as an MA($\infty$) representation, time-reversibility, and closed-forms for the transition probabilities $h$-steps ahead, allowing for coherent forecasting. We analyze time-series counts of skin lesions using our proposed approach, comparing it with existing INAR and INGARCH models. Our model gives more adherence to the data and better forecasting performance.