Forecasting Seasonal Time Series
AbstractThis 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
This chapter was published in:
This item is provided by Elsevier in its series Handbook of Economic Forecasting with number 1-13.
Contact details of provider:
Web page: http://www.elsevier.com/wps/find/bookseriesdescription.cws_home/BS_HE/description
Find related papers by JEL classification:
- B0 - Schools of Economic Thought and Methodology - - General
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Pablo Pincheira, 2011.
"A Bunch of Models, a Bunch of Nulls and Inference About Predictive Ability,"
Working Papers Central Bank of Chile
607, Central Bank of Chile.
- 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.
- 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. 12(1), pages 66-87, April.
- Carlos Medel, 2012.
"How Informative are In–Sample Information Criteria to Forecasting? The Case of Chilean GDP,"
Working Papers Central Bank of Chile
657, Central Bank of Chile.
- 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.
- Medel, Carlos A., 2012. "How informative are in-sample information criteria to forecasting? the case of Chilean GDP," MPRA Paper 35949, University Library of Munich, Germany.
- 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.
- 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.
- Carlos Capistrán & Christian Constandse & Manuel Ramos Francia, 2009. "Using Seasonal Models to Forecast Short-Run Inflation in Mexico," Working Papers 2009-05, Banco de México.
- 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.
If references are entirely missing, you can add them using this form.