Estimation and forecasting of long-memory processes with missing values

Estimation and forecasting of long-memory processes with missing values

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Article ID: iaor19992111
Country: United Kingdom
Volume: 16
Issue: 6
Start Page Number: 395
End Page Number: 410
Publication Date: Nov 1997
Journal: International Journal of Forecasting
Authors: ,
Keywords: ARIMA processes, Kalman filter
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 for 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.

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