Analysis of autoregressive models with symmetric stable innovations

ABSTRACT

This paper develops algorithms for fitting autoregressive models with symmetric stable innovations using auto-covariation function. A recursive algorithm is proposed for generalized Yule-Walker estimation of autoregressive coefficients and partial auto-covariation function. It also introduces a new information criterion, useful for consistent order selection. Applications of the proposed methods are illustrated using observations simulated from autoregressive models with symmetric stable innovations as well as by analysing a set of real data.

KEYWORDS:

• Symmetric stable autoregressive model
• generalized Yule-Walker estimation
• partial auto-covariation function
• information criteria

Classification codes:

• 37M10
• 60G52

Acknowledgements

We thank the referee for several suggestions on the earlier draft of the paper which lead to the present improved version.

Disclosure statement

No potential conflict of interest was reported by the authors.