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Can Forecasting Performance Be Improved by Considering the Steady State?: An Application to Swedish Inflation and Interest Rate
Sveriges Riksbank and Department of Economics, Uppsala University, Uppsala, Sweden.ORCID iD: 0000-0002-4840-7649
2008 (English)In: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131X, Vol. 27, no 1, p. 41-51Article in journal (Refereed) Published
Abstract [en]

This paper investigates whether the forecasting performance of Bayesian autoregressive and vector autoregressive models can be improved by incorporating prior beliefs on the steady state of the time series in the system. Traditional methodology is compared to the new framework — in which a mean-adjusted form of the models is employed — by estimating the models on Swedish inflation and interest rate data from 1980 to 2004. Results show that the out-of-sample forecasting ability of the models is practically unchanged for inflation but significantly improved for the interest rate when informative prior distributions on the steady state are provided. The findings in this paper imply that this new methodology could be useful since it allows us to sharpen our forecasts in the presence of potential pitfalls such as near unit root processesand structural breaks, in particular when relying on small samples.

Place, publisher, year, edition, pages
John Wiley & Sons, 2008. Vol. 27, no 1, p. 41-51
Keywords [en]
out-of-sample forecasts, steady state
National Category
Economics
Research subject
Economics
Identifiers
URN: urn:nbn:se:oru:diva-61069DOI: 10.1002/for.1041ISI: 000253174300003Scopus ID: 2-s2.0-39349116800OAI: oai:DiVA.org:oru-61069DiVA, id: diva2:1141781
Available from: 2017-09-15 Created: 2017-09-15 Last updated: 2017-09-22Bibliographically approved

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Österholm, Pär

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Output format
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  • asciidoc
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