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Estimation and forecasting of long-memory processes with missing values

โœ Scribed by Wilfredo Palma; Ngai Hang Chan

Publisher
John Wiley and Sons
Year
1997
Tongue
English
Weight
264 KB
Volume
16
Category
Article
ISSN
0277-6693

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โœฆ Synopsis

This paper addresses the issues of maximum likelihood estimation and forecasting of a long-memory time series with missing values. A state-space representation of the underlying long-memory process is proposed. By incorporating this representation with the Kalman ยฎlter, the proposed method allows not only for an ecient estimation of an ARFIMA model but also for the estimation of future values under the presence of missing data. This procedure is illustrated through an analysis of a foreign exchange data set. An investment scheme is developed which demonstrates the usefulness of the proposed approach.

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