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Beating the VAR: Improving Swedish GDP Forecasts Using Error and Intercept Corrections
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2015 (English)In: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131X, Vol. 34, no 5, 354-363 p.Article in journal (Refereed) Published
Abstract [en]

This paper examines the forecast accuracy of an unrestricted vector autoregressive (VAR) model for GDP, relative to a comparable vector error correction model (VECM) that recognizes that the data are characterized by co-integration. In addition, an alternative forecast method, intercept correction, is considered for further comparison. Recursive out-of-sample forecasts are generated for both models and forecast techniques. The generated forecasts for each model are objectively evaluated by a selection of evaluation measures and equal accuracy tests. The result shows that the VECM consistently outperforms the VAR models. Further, intercept correction enhances the forecast accuracy when applied to the VECM, whereas there is no such indication when applied to the VAR model. For certain forecast horizons there is a significant difference in forecast ability between the intercept corrected VECM compared to the VAR model.

Place, publisher, year, edition, pages
2015. Vol. 34, no 5, 354-363 p.
Keyword [en]
forecast accuracy, vector error correction, vector autoregressive, co-integration, intercept correction and Diebold-Mariano test
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-260276DOI: 10.1002/for.2329ISI: 000358009600002OAI: oai:DiVA.org:uu-260276DiVA: diva2:847819
Available from: 2015-08-21 Created: 2015-08-18 Last updated: 2017-12-04Bibliographically approved

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Lyhagen, JohanEkberg, Stefan

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