Maximum likelihood estimation for α-stable autoregressive processes

Beth Andrews*, Matthew Calder, Richard A. Davis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

We consider maximum likelihood estimation for both causal and noncausal autoregressive time series processes with non-Gaussian a-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 n1/α-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 n1/2 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.

Original languageEnglish (US)
Pages (from-to)1946-1982
Number of pages37
JournalAnnals of Statistics
Volume37
Issue number4
DOIs
StatePublished - Aug 2009

Keywords

  • Autoregressive models
  • Maximum likelihood estimation
  • Non-gaussian
  • Noncausal
  • Stable distributions

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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