() and Philip Hans Franses
Mårten Löf: Dept. of Economic Statistics, Stockholm School of Economics, Postal: Stockholm School of Economics, P.O. Box 6501, S-113 83 Stockholm, Sweden
Philip Hans Franses: Econometric Institute, Postal: Erasmus University Rotterdam, P.O. Box 1738, DR Rotterdam, The Netherlands, ,
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.
28 pages, January 14, 2000
Full text files
hastef0350.pdf.zip Full text
hastef0350.pdf Full text
hastef0350.ps.zip Full text
hastef0350.ps PostScript file Full text
Questions (including download problems) about the papers in this series should be directed to Helena Lundin ()
Report other problems with accessing this service to Sune Karlsson ().
This page generated on 2018-01-27 00:01:24.