Finding Good Predictors for Inflation: A Bayesian Model Averaging Approach
Tor Jacobson () and
Sune Karlsson ()
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Tor Jacobson: Research Department, Central Bank of Sweden, Postal: Sveriges Riksbank, SE-103 37 Stockholm, Sweden
No 138, Working Paper Series from Sveriges Riksbank (Central Bank of Sweden)
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.
Keywords: Variable selection; Markov chain Monte Carlo; Forecast (search for similar items in EconPapers)
JEL-codes: C11 C51 C52 C53 (search for similar items in EconPapers)
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Published in Journal of Forecasting, 2004, pages 479-496.
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Journal Article: Finding good predictors for inflation: a Bayesian model averaging approach (2004)
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