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VOL. 52 | 2006 Estimation of AR and ARMA models by stochastic complexity
Ciprian Doru Giurčaneanu, Jorma Rissanen

Editor(s) Hwai-Chung Ho, Ching-Kang Ing, Tze Leung Lai

Abstract

In this paper the stochastic complexity criterion is applied to estimation of the order in AR and ARMA models. The power of the criterion for short strings is illustrated by simulations. It requires an integral of the square root of Fisher information, which is done by Monte Carlo technique. The stochastic complexity, which is the negative logarithm of the Normalized Maximum Likelihood universal density function, is given. Also, exact asymptotic formulas for the Fisher information matrix are derived.

Information

Published: 1 January 2006
First available in Project Euclid: 28 November 2007

zbMATH: 1268.62113
MathSciNet: MR2427838

Digital Object Identifier: 10.1214/074921706000000941

Subjects:
Primary: 62B10
Secondary: 91B70

Keywords: Fisher information , minimum description length principle , Monte Carlo technique , normalized maximum likelihood universal model