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On Forecasting Cointegrated Seasonal Time Series

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Author Info
Löf, Mårten () (Dept. of Economic Statistics, Stockholm School of Economics)
Franses, Philip Hans () (Econometric Institute)

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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.

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Publisher Info
Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 350.

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Length: 28 pages
Date of creation: 14 Jan 2000
Date of revision:
Publication status: Forthcoming in International Journal of Forecasting.
Handle: RePEc:hhs:hastef:0350

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Related research
Keywords: Periodic Cointegration; Seasonal cointegration; Forecasting;

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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References listed on IDEAS
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  1. Peter Boswijk & Philip Hans Franses & Niels Haldrup, 1995. "Multiple Unit Roots in Periodic Autoregression," University of California at San Diego, Economics Working Paper Series 95-44, Department of Economics, UC San Diego.
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  2. Hylleberg, S. & Engle, R. F. & Granger, C. W. J. & Yoo, B. S., 1990. "Seasonal integration and cointegration," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 215-238. [Downloadable!] (restricted)
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  3. Lee, Hahn Shik & Siklos, Pierre L., 1997. "The role of seasonality in economic time series reinterpreting money-output causality in U.S. data," International Journal of Forecasting, Elsevier, vol. 13(3), pages 381-391, September. [Downloadable!] (restricted)
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Cited by:
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  1. Cubadda, Gianluca & Omtzigt, Pieter, 2003. "Small Sample Improvements in the Statistical Analysis of Seasonally Cointegrated Systems," Economics & Statistics Discussion Papers esdp03012, University of Molise, Dept. SEGeS. [Downloadable!]
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  2. P.H. Franses & D. Van Dijk, 2001. "The Forecasting Performance of Various Models for Seasonality and Nonlinearity for Quarterly Industrial Production," Econometric Institute Report 222, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
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