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Modelling time series with season-dependent autocorrelation structure

โœ Scribed by Yorghos Tripodis; Jeremy Penzer

Publisher
John Wiley and Sons
Year
2009
Tongue
English
Weight
225 KB
Volume
28
Category
Article
ISSN
0277-6693

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โœฆ Synopsis

Abstract

Time series with seasonโ€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.โ€ƒCopyright ยฉ 2008 John Wiley & Sons, Ltd.

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