Geometric Infinitely Divisible Autoregressive Models

TL;DR

This paper introduces geometric infinitely divisible autoregressive models, exploring their properties, distributional behaviors, and parameter estimation methods, extending to higher-order processes.

Contribution

It presents new autoregressive models with gid marginals, generalizes to kth order, and develops estimation techniques based on least squares and moments.

Findings

Distributional properties at 0+ analyzed

Parameter estimation methods proposed

Extension to kth order AR processes

Abstract

In this article, we discuss some geometric infinitely divisible (gid) random variables using the Laplace exponents which are Bernstein functions and study their properties. The distributional properties and limiting behavior of the probability densities of these gid random variables at 0+ are studied. The autoregressive (AR) models with gid marginals are introduced. Further, the first order AR process is generalised to kth order AR process. We also provide the parameter estimation method based on conditional least square and method of moments for the introduced AR(1) processes.