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
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.
|Additional Information:||© 2009 John Wiley & Sons, Inc.|
|Library of Congress subject classification:||Q Science > QA Mathematics|
|Sets:||Departments > Statistics|
|Identification Number:||UT ISI:000271842800001|
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