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Modelling time series with season-dependent autocorrelation structure

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

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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.
Subjects: Q Science > QA Mathematics
Sets: Departments > Statistics
Identification Number: 10.1002/for.1106
Date Deposited: 11 Jan 2010 10:11
Last Modified: 04 May 2017 09:36
URI: http://eprints.lse.ac.uk/id/eprint/26637

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