The Annals of Statistics

A Bayes Procedure for the Identification of Univariate Time Series Models

D. S. Poskitt

Full-text: Open access

Abstract

This paper is concerned with model selection in time series analysis. An identification criterion is presented that is asymptotically equivalent to a Bayes decision rule. The discussion is conducted in the context of a general class of parametric time series models and consideration is given to the special case of order determination in autoregressive moving-average representations. Consistency of the criterion is proved.

Article information

Source
Ann. Statist., Volume 14, Number 2 (1986), 502-516.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176349935

Digital Object Identifier
doi:10.1214/aos/1176349935

Mathematical Reviews number (MathSciNet)
MR840511

Zentralblatt MATH identifier
0603.62093

JSTOR
links.jstor.org

Subjects
Primary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Secondary: 62C10: Bayesian problems; characterization of Bayes procedures

Keywords
Time series model power spectrum autoregressive moving-average representation Bayes decision rule model selection criterion consistency

Citation

Poskitt, D. S. A Bayes Procedure for the Identification of Univariate Time Series Models. Ann. Statist. 14 (1986), no. 2, 502--516. doi:10.1214/aos/1176349935. https://projecteuclid.org/euclid.aos/1176349935


Export citation