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April 1998 State space modeling of long-memory processes
Ngai Hang Chan, Wilfredo Palma
Ann. Statist. 26(2): 719-740 (April 1998). DOI: 10.1214/aos/1028144856

Abstract

This paper develops a state space modeling for long-range dependent data. Although a long-range dependent process has an infinite-dimensional state space representation, it is shown that by using the Kalman filter, the exact likelihood function can be computed recursively in a finite number of steps. Furthermore, an approximation to the likelihood function based on the truncated state space equation is considered. Asymptotic properties of these approximate maximum likelihood estimates are established for a class of long-range dependent models, namely, the fractional autoregressive moving average models. Simulation studies show rapid converging properties of the approximate maximum likelihood approach.

Citation

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Ngai Hang Chan. Wilfredo Palma. "State space modeling of long-memory processes." Ann. Statist. 26 (2) 719 - 740, April 1998. https://doi.org/10.1214/aos/1028144856

Information

Published: April 1998
First available in Project Euclid: 31 July 2002

zbMATH: 0929.62091
MathSciNet: MR1626083
Digital Object Identifier: 10.1214/aos/1028144856

Subjects:
Primary: 62E20, 62M10
Secondary: 60F17

Rights: Copyright © 1998 Institute of Mathematical Statistics

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Vol.26 • No. 2 • April 1998
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