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Model Identification for Infinite Variance Autoregressive Processes

Summary: Model Identification for Infinite Variance
Autoregressive Processes
Beth Andrews
Northwestern University
Richard A. Davis
Columbia University
June 17, 2011
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.

Corresponding author. Department of Statistics, Northwestern University, 2006 Sheridan Road, Evanston, IL
60208, USA. Telephone: 1 847 467 4533. E-mail: bandrews@northwestern.edu.
JEL classification codes. C13, C22.
Keywords. Akaike's information criterion, all-pass models, autoregressive processes, infinite variance, noncausal.


Source: Andrews, Beth - Department of Statistics, Northwestern University


Collections: Mathematics