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. © 1997 John Wiley & Sons, Ltd.
| Original language | English |
|---|---|
| Pages (from-to) | 395-410 |
| Journal | Journal of Forecasting |
| Volume | 16 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1997 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- ARFIMA models
- Forecasting
- Foreign exchange data
- Long memory
- Maximum likelihood estimation
- Missing values
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