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Seasonal Specific Structural Time Series

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  • Proietti Tommaso

    ()
    (University of Udine, Italy)

Abstract

The paper introduces the class of seasonal specific structural time series models, according to which each season follows specific dynamics, but is also tied to the others by a common random effect. Seasonal specific models are dynamic variance components models that account for some kind of periodic behaviour, such as periodic heteroscedasticity, and are also tailored to deal with situations such that one or a group of seasons behave differently. Trends and non periodic features can still be extracted and their nature is discussed. Multivariate extensions entertain the case when cointegration pertains only to groups of seasons. It is finally shown that a circular correlation pattern for the idiosyncratic disturbances yields a periodic component that is isomorphic to a trigonometric seasonal com- ponent.

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Bibliographic Info

Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 8 (2004)
Issue (Month): 2 (May)
Pages: 1-22

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Handle: RePEc:bpj:sndecm:v:8:y:2004:i:2:n:16

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Web page: http://www.degruyter.com

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Cited by:
  1. Siem Jan Koopman & Marius Ooms & Irma Hindrayanto, 2009. "Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 683-713, October.
  2. Philip Kostov & John Lingard, 2005. "Seasonally specific model analysis of UK cereals prices," Econometrics 0507014, EconWPA.
  3. Balogh, Peter & Kovacs, Sandor & Chaiboonsri, Chukiat & Chaitip, Prasert, 2009. "Forecasting with X-12-ARIMA: International tourist arrivals to India and Thailand," APSTRACT: Applied Studies in Agribusiness and Commerce, AGRIMBA, vol. 3.
  4. Prasert Chaitip & Chukiat Chaiboonsri & N. Rangaswamy & Siriporn Mcdowall, 2009. "Forecasting with X-12-Arima: International Tourist Arrivals to India," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 9(1), pages 107-128.
  5. Koopman, Siem Jan & Ooms, Marius, 2006. "Forecasting daily time series using periodic unobserved components time series models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 885-903, November.
  6. Prasert Chaitip & Chukiat Chaiboonsri, 2009. "Down Trend Forecasting Method with ARFIMA: International Tourist Arrivals to Thailand," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 9(1), pages 143-150.
  7. Prasert Chaitip & Chukiat Chaiboonsri, 2009. "Forecasting with X-12-ARIMA and ARFIMA: International Tourist Arrivals to India," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 9(3), pages 147-162.

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