Modelling time series with season-dependent autocorrelation structure

Yorghos Tripodis and Jeremy Penzer
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Yorghos Tripodis: Department of Biostatistics, Boston University, Boston, Massachusetts, USA, Postal: Department of Biostatistics, Boston University, Boston, Massachusetts, USA
Jeremy Penzer: Department of Statistics, LSE, London, UK, Postal: Department of Statistics, LSE, London, UK

Journal of Forecasting, 2009, vol. 28, issue 7, 559-574

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.

Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:28:y:2009:i:7:p:559-574

DOI: 10.1002/for.1106

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