Tony Chernis, Niko Hauzenberger, Florian Huber, Gary Koop, James Mitchell
arXiv 21 Nov 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2311.12671 · PDF · DOI · OpenAlex · Extracted main text
Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in BPS is a “synthesis” function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from the euro area's Survey of Professional Forecasters. The second combines density forecasts of US inflation produced by many regression models involving different predictors. Both applications demonstrate the benefits -- in terms of improved forecast accuracy and interpretability -- of modeling the synthesis function nonparametrically.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Diebold, Francis X., Minchul Shin, and Boyuan Zhang (2023) On the aggregation of probability assessments: Regularized mixtures of predictive densities for Eurozone inflation and real inte… | 1.000 | 6 | 3 | 100% |
| 2 | McAlinn, Kenichiro and Mike West (2019) Dynamic Bayesian predictive synthesis in time series forecasting | 1.000 | 6 | 3 | 100% |
| 3 | Aastveit, Knut Are, Jamie L. Cross, and Herman K. van Dijk (2023) Quantifying time-varying forecast uncertainty and risk for the real price of oil | 0.811 | 4 | 2 | 100% |
| 4 | Chipman, Hugh A., Edward I. George, and Robert E. McCulloch (2010) BART: Bayesian additive regression trees | 0.794 | 10 | 3 | 50% |
| 5 | Oelrich, Oscar, Mattias Villani, and Sebastian Ankargren (2023) Local prediction pools | 0.737 | 3 | 2 | 100% |
| 6 | Chernis, Tony (2023) Combining large numbers of density predictions with Bayesian Predictive Synthesis self | 0.737 | 3 | 2 | 100% |
| 7 | Giacomini, Raffaella and Barbara Rossi (2010) Forecast comparisons in unstable environments | 0.644 | 5 | 2 | 40% |
| 8 | Diebold, Francis X and Roberto S Mariano (1995) Comparing predictive accuracy | 0.644 | 4 | 2 | 50% |
| 9 | Hauzenberger, Niko, Florian Huber, Gary Koop, and Luca Onorante (2022) Fast and flexible Bayesian inference in time-varying parameter regression models self | 0.644 | 3 | 2 | 67% |
| 10 | Aastveit, Knut Are, James Mitchell, Francesco Ravazzolo, and Herman… (2019) The evolution of forecast density combinations in economics self | 0.644 | 2 | 2 | 100% |
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