Model identification for infinite variance autoregressive processes

Beth Andrews*, Richard A. Davis

*Corresponding author for this work

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

We consider model identification for infinite variance autoregressive time series processes. It is shown that a consistent estimate of autoregressive model order can be obtained by minimizing Akaike's information criterion, and we use all-pass models to identify noncausal autoregressive processes and estimate the order of noncausality (the number of roots of the autoregressive polynomial inside the unit circle in the complex plane). We examine the performance of the order selection procedures for finite samples via simulation, and use the techniques to fit a noncausal autoregressive model to stock market trading volume data.

Original languageEnglish (US)
Pages (from-to)222-234
Number of pages13
JournalJournal of Econometrics
Volume172
Issue number2
DOIs
StatePublished - Feb 2013

Keywords

  • Akaike's information criterion
  • All-pass models
  • Autoregressive processes
  • Infinite variance
  • Noncausal

ASJC Scopus subject areas

  • Economics and Econometrics

Fingerprint Dive into the research topics of 'Model identification for infinite variance autoregressive processes'. Together they form a unique fingerprint.

  • Cite this