Tripodis, Yorghos and Penzer, Jeremy (2009) Modelling time series with season-dependent autocorrelation structure. Journal of Forecasting, 28 (7). pp. 559-574. ISSN 0277-6693
Full text not available from this repository.Abstract
Time series with season-dependent autocorrelation structure are commonly modelled using periodic autoregressive moving average (PARMA) processes. In most applications, the moving average terms are excluded for ease of estimation. We propose a new class of periodic unobserved component models (PUCM). Parameter estimates for PUCM are readily interpreted; the estimated coefficients correspond to variances of the measurement noise and of the error terms in unobserved components. We show that PUCM have correlation structure equivalent to that of a periodic integrated moving average (PIMA) process. Results from practical applications indicate that our models provide a natural framework for series with periodic autocorrelation structure both in terms of interpretability and forecasting accuracy. Copyright © 2008 John Wiley & Sons, Ltd.
| Item Type: | Article |
|---|---|
| Official URL: | http://www3.interscience.wiley.com/journal/2966/ho... |
| Additional Information: | © 2009 John Wiley & Sons, Inc. |
| Divisions: | Statistics |
| Subjects: | Q Science > QA Mathematics |
| Date Deposited: | 11 Jan 2010 10:11 |
| Last Modified: | 11 Dec 2024 23:30 |
| URI: | http://eprints.lse.ac.uk/id/eprint/26637 |
Actions (login required)
 |
View Item |
