Article2007

Forecasting Using Bayesian and Information-Theoretic Model Averaging

64 citations0/0 files storedJournal of Business and Economic Statistics

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Forecasting Techniques and Applications Statistical Methods and Bayesian Inference Statistical Methods and Inference

Abstract

Model averaging often improves forecast accuracy over individual forecasts. It may also be seen as a means of forecasting in data-rich environments. Bayesian model averaging methods have been widely advocated, but a neglected frequentist approach is to use information-theoretic-based weights. We consider the use of information-theoretic model averaging in forecasting U.K. inflation, with a large dataset, and find that it can be a powerful alternative to Bayesian averaging schemes.

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References

1994Comparing Predictive Accuracy 1969The Combination of Forecasts 2002Macroeconomic Forecasting Using Diffusion Indexes 2000Forecasting Economic Time Series 2000The Generalized Dynamic-Factor Model: Identification and Estimation 1998Evaluating Density Forecasts with Applications to Financial Risk Management 1999Forecasting inflation 2004Combination forecasts of output growth in a seven‐country data set

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2011The performance of short-term forecasts of the German economy before and during the 2008/2009 recession 2015Forecasting Inflation with the Phillips Curve: A Dynamic Model Averaging Approach for Brazil

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