IDEAS home Printed from
   My bibliography  Save this book chapter

Forecasting Seasonal Time Series

In: Handbook of Economic Forecasting


  • Ghysels, Eric
  • Osborn, Denise R.
  • Rodrigues, Paulo M.M.


This chapter reviews the principal methods used by researchers when forecasting seasonal time series. In addition, the often overlooked implications of forecasting and feedback for seasonal adjustment are discussed. After an introduction in Section 1, Section 2 examines traditional univariate linear models, including methods based on SARIMA models, seasonally integrated models and deterministic seasonality models. As well as examining how forecasts are computed in each case, the forecast implications of misspecifying the class of model (deterministic versus nonstationary stochastic) are considered. The linear analysis concludes with a discussion of the nature and implications of cointegration in the context of forecasting seasonal time series, including merging short-term seasonal forecasts with those from long-term (nonseasonal) models. Periodic (or seasonally varying parameter) models, which often arise from theoretical models of economic decision-making, are examined in Section 3. As periodic models may be highly parameterized, their value for forecasting can be open to question. In this context, modelling procedures for periodic models are critically examined, as well as procedures for forecasting. Section 3 discusses less traditional models, specifically nonlinear seasonal models and models for seasonality in variance. Such nonlinear models primarily concentrate on interactions between seasonality and the business cycle, either using a threshold specification to capture changing seasonality over the business cycle or through regime transition probabilities being seasonally varying in a Markov switching framework. Seasonality heteroskedasticity is considered for financial time series, including deterministic versus stochastic seasonality, periodic GARCH and periodic stochastic volatility models for daily or intra-daily series. Economists typically consider that seasonal adjustment rids their analysis of the "nuisance" of seasonality. Section 5 shows this to be false. Forecasting seasonal time series is an inherent part of seasonal adjustment and, further, decisions based on seasonally adjusted data affect future outcomes, which destroys the assumed orthogonality between seasonal and nonseasonal components of time series.

Suggested Citation

  • Ghysels, Eric & Osborn, Denise R. & Rodrigues, Paulo M.M., 2006. "Forecasting Seasonal Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 13, pages 659-711, Elsevier.
  • Handle: RePEc:eee:ecofch:1-13

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Nicolás Chanut & Mario Marcel C. & Carlos A. Medel V., 2019. "Can economic perception surveys improve macroeconomic forecasting in Chile?," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 22(3), pages 034-097, December.
    2. Pablo M. Pincheira & Carlos A. Medel, 2016. "Forecasting with a Random Walk," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(6), pages 539-564, December.
    3. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    4. Carlos A. Medel, 2013. "How informative are in-sample information criteria to forecasting? The case of Chilean GDP," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 50(1), pages 133-161, May.
    5. Carlos A. Medel, 2018. "Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach," International Economic Journal, Taylor & Francis Journals, vol. 32(3), pages 331-371, July.
    6. Pincheira, Pablo, 2013. "A Bunch of Models, a Bunch of Nulls and Inference about Predictive Ability," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 26-43, October.
    7. Alexander Vosseler & Enzo Weber, 2018. "Forecasting seasonal time series data: a Bayesian model averaging approach," Computational Statistics, Springer, vol. 33(4), pages 1733-1765, December.
    8. Jonathan H. Wright, 2013. "Unseasonal Seasonals?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(2 (Fall)), pages 65-126.
    9. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
    10. Capistrán, Carlos & Constandse, Christian & Ramos-Francia, Manuel, 2010. "Multi-horizon inflation forecasts using disaggregated data," Economic Modelling, Elsevier, vol. 27(3), pages 666-677, May.
    11. Pablo Pincheira & Carlos A. Medel, 2012. "Forecasting Inflation with a Simple and Accurate Benchmark: a Cross-Country Analysis," Working Papers Central Bank of Chile 677, Central Bank of Chile.
    12. Pablo Pincheira Brown & Álvaro García Marín, 2009. "Forecasting Inflation in Chile With an Accurate Benchmark," Working Papers Central Bank of Chile 514, Central Bank of Chile.
    13. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911.
    14. Daniel Dzikowski & Carsten Jentsch, 2024. "Structural Periodic Vector Autoregressions," Papers 2401.14545,
    15. Carlos Medel, 2021. "Forecasting Brazilian Inflation with the Hybrid New Keynesian Phillips Curve: Assessing the Predictive Role of Trading Partners," Working Papers Central Bank of Chile 900, Central Bank of Chile.
    16. Phillip M. Yelland & Shinji Kim & Renée Stratulate, 2010. "A Bayesian Model for Sales Forecasting at Sun Microsystems," Interfaces, INFORMS, vol. 40(2), pages 118-129, April.
    17. Marcus Cobb, 2009. "Forecasting Chilean Inflation From Disaggregate Components," Working Papers Central Bank of Chile 545, Central Bank of Chile.
    18. Pablo Pincheira B., 2014. "Predictive Evaluation of Sectoral and Total Employment Based on Entrepreneurial Confidence Indicators," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 17(1), pages 66-87, April.
    19. Capistrán Carlos & Constandse Christian & Ramos Francia Manuel, 2009. "Using Seasonal Models to Forecast Short-Run Inflation in Mexico," Working Papers 2009-05, Banco de México.
    20. Pablo M. Pincheira & Carlos A. Medel, 2015. "Forecasting Inflation with a Simple and Accurate Benchmark: The Case of the US and a Set of Inflation Targeting Countries," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 65(1), pages 2-29, January.
    21. Carlos Medel, 2021. "Searching for the Best Inflation Forecasters within a Consumer Perceptions Survey: Microdata Evidence from Chile," Working Papers Central Bank of Chile 899, Central Bank of Chile.
    22. Pablo Pincheira & Carlos Medel, 2012. "Forecasting Inflation With a Random Walk," Working Papers Central Bank of Chile 669, Central Bank of Chile.

    More about this item

    JEL classification:

    • B0 - Schools of Economic Thought and Methodology - - General


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecofch:1-13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.