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: | Palma Wilfredo, Chan Ngai Hang |
Keywords: | ARIMA processes, Kalman filter |
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.