Maximum likelihood estimation for \(\alpha\)-stable autoregressive processes

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Bibliographic Details
Published in:	arXiv.org (Aug 13, 2009), p. n/a
Main Author:	Andrews, Beth
Other Authors:	Calder, Matthew, Davis, Richard A
Published:	Cornell University Library, arXiv.org
Subjects:	Monte Carlo simulation Noise Economic models Logarithms Convergence Parameter estimation Constraining Autoregressive processes Gaussian process Maximum likelihood estimation Asymptotic properties Autoregressive models Stock exchanges Maximum likelihood estimators Computer simulation
Online Access:	Citation/Abstract Full text outside of ProQuest
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Description
Abstract:	We consider maximum likelihood estimation for both causal and noncausal autoregressive time series processes with non-Gaussian \(\alpha\)-stable noise. A nondegenerate limiting distribution is given for maximum likelihood estimators of the parameters of the autoregressive model equation and the parameters of the stable noise distribution. The estimators for the autoregressive parameters are \(n^{1/\alpha}\)-consistent and converge in distribution to the maximizer of a random function. The form of this limiting distribution is intractable, but the shape of the distribution for these estimators can be examined using the bootstrap procedure. The bootstrap is asymptotically valid under general conditions. The estimators for the parameters of the stable noise distribution have the traditional \(n^{1/2}\) rate of convergence and are asymptotically normal. The behavior of the estimators for finite samples is studied via simulation, and we use maximum likelihood estimation to fit a noncausal autoregressive model to the natural logarithms of volumes of Wal-Mart stock traded daily on the New York Stock Exchange.
ISSN:	2331-8422
DOI:	10.1214/08-AOS632
Source:	Engineering Database