Finding Good Predictors for Inflation: A Bayesian Model Averaging Approach
AbstractWe 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.
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Bibliographic InfoPaper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 138.
Length: 24 pages
Date of creation: 01 Aug 2002
Date of revision:
Publication status: Published in Journal of Forecasting, 2004, pages 479-496.
Variable selection; Markov chain Monte Carlo; Forecast;
Other versions of this item:
- Sune Karlsson & Tor Jacobson, 2004. "Finding good predictors for inflation: a Bayesian model averaging approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 479-496.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2002-08-29 (All new papers)
- NEP-ECM-2002-08-29 (Econometrics)
- NEP-ETS-2002-08-29 (Econometric Time Series)
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