The modified Yule-Walker method for multidimensional infinite-variance periodic autoregressive model of order 1

TL;DR

This paper introduces a novel estimation method for multidimensional infinite-variance periodic autoregressive models using covariation measures, validated through simulations and real data analysis.

Contribution

It extends classical PAR models to infinite-variance distributions by replacing covariance with covariation and proposes two estimation techniques based on different covariation measures.

Findings

The covariation-based estimators perform well in simulations.

Spectral and moment-based methods are comparable in effectiveness.

Application to real data demonstrates practical utility.

Abstract

The time series with periodic behavior, such as the periodic autoregressive (PAR) models belonging to the class of the periodically correlated processes, are present in various real applications. In the literature, such processes were considered in different directions, especially with the Gaussian-distributed noise. However, in most of the applications, the assumption of the finite-variance distribution seems to be too simplified. Thus, one can consider the extensions of the classical PAR model where the non-Gaussian distribution is applied. In particular, the Gaussian distribution can be replaced by the infinite-variance distribution, e.g. by the $\alpha-$stable distribution. In this paper, we focus on the multidimensional $\alpha-$stable PAR time series models. For such models, we propose a new estimation method based on the Yule-Walker equations. However, since for the infinite-variance case the covariance does not exist, thus it is replaced by another measure, namely the covariation. In this paper we propose to apply two estimators of the covariation measure. The first one is based on moment representation (moment-based) while the second one - on the spectral measure representation (spectral-based). The validity of the new approaches are verified using the Monte Carlo simulations in different contexts, including the sample size and the index of stability of the noise. Moreover, we compare the moment-based covariation-based method with spectral-based covariation-based technique. Finally, the real data analysis is presented.