SSE/EFI Working Paper Series in Economics and Finance
On Forecasting Cointegrated Seasonal Time Series
() and Philip Hans Franses
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
Keywords: Periodic Cointegration; Seasonal cointegration; Forecasting; (follow links to similar papers)
JEL-Codes: C32; C53; (follow links to similar papers)
28 pages, January 14, 2000
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- This paper is forthcoming as:
Löf, Mårten and Philip Hans Franses, 'On Forecasting Cointegrated Seasonal Time Series', International Journal of Forecasting.
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