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The Estimation of Continuous Parameter Long-Memory Time Series Models

Published online by Cambridge University Press:  11 February 2009

Marcus J. Chambers
Marcus J. Chambers
Affiliation:
University of Essex
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Abstract

A class of univariate fractional ARIMA models with a continuous time parameter is developed for the purpose of modeling long-memory time series. The spectral density of discretely observed data is derived for both point observations (stock variables) and integral observations (flow variables). A frequency domain maximum likelihood method is proposed for estimating the longmemory parameter and is shown to be consistent and asymptotically normally distributed, and some issues associated with the computation of the spectral density are explored.

Information

Type
Research Article
Information
Econometric Theory , Volume 12 , Issue 2 , June 1996 , pp. 374 - 390
Copyright
Copyright © Cambridge University Press 1996

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References

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