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Finding Good Predictors for Inflation: A Bayesian Model Averaging Approach
Sveriges Riksbank, Stockholm, Sweden.
Stockholm School of Economics, Stockholm, Sweden. (Stat@oru)ORCID iD: 0000-0003-0203-4688
2004 (English)In: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131X, Vol. 23, no 7, 479-496 p.Article in journal (Refereed) Published
Abstract [en]

We consider a Bayesian model averaging approach for the purpose of forecasting Swedish consumer price index inflation using a large set of potential indicators, comprising some 80 quarterly time series covering a wide spectrum of Swedish economic activity. The paper demonstrates how to efficiently and systematically evaluate (almost) all possible models that these indicators in combination can give rise to. The results, in terms of out-of-sample performance, suggest that Bayesian model averaging is a useful alternative to other forecasting procedures, in particular recognizing the flexibility by which new information can be incorporated.

Place, publisher, year, edition, pages
John Wiley & Sons, 2004. Vol. 23, no 7, 479-496 p.
Keyword [en]
variable selection; Markov chain Monte Carlo; forecast
National Category
Economics Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-61128DOI: 10.1002/for.924ISI: 000225169500002Scopus ID: 2-s2.0-9744265710OAI: oai:DiVA.org:oru-61128DiVA: diva2:1144106
Available from: 2017-09-25 Created: 2017-09-25 Last updated: 2017-09-26Bibliographically approved

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CiteExportLink to record
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