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On seasonal error correction when the processes include different numbers of unit roots
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Information Science.
2003 (English)In: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131X, Vol. 22, no 5, 377-389 p.Article in journal (Refereed) Published
Abstract [en]

We propose a seasonal cointegration model (SECM) for quarterly data which includes variables with different numbers of unit roots and thus needs to be transformed in different ways in order to yield stationarity. A Monte Carlo simulation is carried out to investigate the consequences of specifying a SECM with all variables in annual diffrerences in this situation. The SECM in annual differences is compared to the correctly specified model. Pre-testing for unit roots using two different approaches, and where the models are specified according to the unit root test results, is also considered. The forecast mean squared error criterion and certain parameter estimation results indicate that, in practice, a cointegration model where all variables are transformed with the annual difference filter is more robust than one obtained by pre-testing for a smaller number of unit roots. The second-best choice when the true model is not known and when the aim is to forecast, is an ordinary VAR model also in annual differences.

Place, publisher, year, edition, pages
2003. Vol. 22, no 5, 377-389 p.
National Category
Social Sciences
URN: urn:nbn:se:uu:diva-46985DOI: 10.1002/for.864ISI: 000184851400002OAI: oai:DiVA.org:uu-46985DiVA: diva2:74892
Available from: 2008-10-17 Created: 2008-10-17 Last updated: 2012-02-09Bibliographically approved

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Lyhagen, Johan
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