Affiliation:
1. Bank of Canada , K1A 0G9 , Ottawa , Canada
Abstract
Abstract
Bayesian Predictive Synthesis is a flexible method of combining density predictions. The flexibility comes from the ability to choose an arbitrary synthesis function to combine predictions. I study choice of synthesis function when combining large numbers of predictions – a common occurrence in macroeconomics. Estimating combination weights with many predictions is difficult, so I consider shrinkage priors and factor modelling techniques to address this problem. These techniques provide an interesting contrast between the sparse weights implied by shrinkage priors and dense weights of factor modelling techniques. I find that the sparse weights of shrinkage priors perform well across exercises.
Subject
Economics and Econometrics,Social Sciences (miscellaneous),Analysis,Economics and Econometrics,Social Sciences (miscellaneous),Analysis
Reference57 articles.
1. Aastveit, K. A., K. Gerdrup, and A. S. Jore. 2011. Short-term Forecasting of GDP and Inflation in Real-Time: Norges Bank’s System for Averaging Models, Vol.9. Oslo: Norges Bank Staff Memo.
2. Aastveit, K. A., F. Ravazzolo, and H. K. Van Dijk. 2016. “Combined Density Nowcasting in an Uncertain Economic Environment.” Journal of Business & Economic Statistics 36 (1): 131–45.
3. Aastveit, K. A., J. Mitchell, F. Ravazzolo, and H. K. Van Dijk. 2018. The Evolution of Forecast Density Combinations in Economics. Tinbergen Institute. Technical Report 18-069/III.
4. Aastveit, K. A., J. L. Cross, and H. K. V. Dijk. 2023. “Quantifying Time-Varying Forecast Uncertainty and Risk for the Real Price of Oil.” Journal of Business & Economic Statistics 41 (2): 523–37. https://doi.org/10.1080/07350015.2022.2039159.
5. Bache, I. W., J. Mitchell, F. Ravazzolo, and S. P. Vahey. 2009. Macro Modelling with Many Models. Norges Bank. Technical Report 2009/15.