Estimation and forecasting of long-memory processes with missing values
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Date
1997
Journal Title
Journal ISSN
Volume Title
Publisher
JOHN WILEY & SONS LTD
Abstract
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 filter, the proposed method allows not only fbr an efficient 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. (C) 1997 John Wiley & Sons, Ltd.
Description
Keywords
long memory, ARFIMA models, forecasting, maximum likelihood estimation, missing values, foreign exchange data, TIME-SERIES, MODELS