Tempered Stable Autoregressive Models

TL;DR

This paper introduces a new one-sided tempered stable autoregressive model (TAR(1)), analyzes its properties, proposes parameter estimation methods, and demonstrates its effectiveness on simulated and real data, generalizing existing models.

Contribution

The paper presents a novel TAR(1) model with tempered stable innovations, including its distributional properties and estimation techniques, extending previous inverse Gaussian and stable autoregressive models.

Findings

The error distribution is infinitely divisible.

Parameter estimation methods perform well on simulated data.

Model fits real and simulated data effectively.

Abstract

In this article, we introduce and study a one sided tempered stable first order autoregressive model called TAR(1). Under the assumption of stationarity of the model, the marginal probability density function of the error term is found. It is shown that the distribution of the error term is infinitely divisible. Parameter estimation of the introduced TAR(1) process is done by adopting the conditional least square and method of moments based approach and the performance of the proposed methods are evaluated on simulated data. Also we study an autoregressive model of order one with tempered stable innovations. Using appropriate test statistic it is shown that the model fit very well on real and simulated data. Our models generalize the inverse Gaussian and one-sided stable autoregressive models existing in the literature.