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 language | English (US) |
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Pages (from-to) | 222-234 |
Number of pages | 13 |
Journal | Journal of Econometrics |
Volume | 172 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2013 |
Funding
We gratefully acknowledge the support of NSF Grants DMS0806104 (Andrews) and DMS0743459 (Davis), and thank two anonymous reviewers for their helpful comments.
Keywords
- Akaike's information criterion
- All-pass models
- Autoregressive processes
- Infinite variance
- Noncausal
ASJC Scopus subject areas
- Economics and Econometrics