On Forecasting Cointegrated Seasonal Time Series

Mårten Löf () and Philip Hans Franses
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Mårten Löf: Dept. of Economic Statistics, Stockholm School of Economics, Postal: Stockholm School of Economics, P.O. Box 6501, S-113 83 Stockholm, Sweden

No 350, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

Abstract: We analyze periodic and seasonal cointegration models for bivariate quarterly observed time series in an empirical forecasting study. We include both single equation and multiple equation methods. A VAR model in first differences with and without cointegration restrictions is also included in the analysis, where it serves as a benchmark. Our empirical results indicate that the VAR model in first differences without cointegration is best if one-step and four-step ahead forecasts are considered. For longer forecast horizons, however, the periodic and seasonal cointegration models are better. When comparing periodic versus seasonal cointegration models, we find that the seasonal cointegration models tend to yield better forecasts. Finally, there is no clear indication that multiple equation methods improve on single equation methods.

Keywords: Periodic Cointegration; Seasonal cointegration; Forecasting (search for similar items in EconPapers)
JEL-codes: C32 C53 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2000-01-14
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (5)

Forthcoming in International Journal of Forecasting.

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Related works:
Journal Article: On forecasting cointegrated seasonal time series (2001)
Working Paper: On forecasting cointegrated seasonal time series (2000)
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