Gael M. Martin, Rubén Loaiza-Maya, David T. Frazier, Worapree Maneesoonthorn, Andrés Ramírez Hassan
arXiv 21 Sep 2020 · Econometrics · publishedInternational Journal of Forecasting (2021) · 14 citations (OpenAlex)
arXiv:2009.09592 · PDF · DOI · OpenAlex · Extracted main text
Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Herein, we re-investigate the practice of using proper scoring rules to produce probabilistic forecasts that are `optimal' according to a given score, and assess when their out-of-sample accuracy is superior to alternative forecasts, according to that score. Particular attention is paid to relative predictive performance under misspecification of the predictive model. Using numerical illustrations, we document several novel findings within this paradigm that highlight the important interplay between the true data generating process, the assumed predictive model and the scoring rule. Notably, we show that only when a predictive model is sufficiently compatible with the true process to allow a particular score criterion to reward what it is designed to reward, will this approach to forecasting reap benefits. Subject to this compatibility however, the superiority of the optimal forecast will be greater, the greater is the degree of misspecification. We explore these issues under a range of different scenarios, and using both artificially simulated and empirical data.
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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 | Gneiting, T. and Raftery, A (2007) Strictly proper scoring rules, prediction, and estimation | 1.000 | 6 | 3 | 100% |
| 2 | Patton, A. J (2019) Comparing possibly misspecified forecasts | 0.874 | 6 | 2 | 100% |
| 3 | Diks, C., Panchenko, V., and van Dijk, D (2011) Likelihood-based scoring rules for comparing density forecasts in tails | 0.644 | 2 | 2 | 100% |
| 4 | Geweke, J. and Amisano, G (2011) Optimal prediction pools | 0.644 | 2 | 2 | 100% |
| 5 | Giacomini, R. and White, H (2006) Tests of conditional predictive ability | 0.644 | 2 | 2 | 100% |
| 6 | Holzmann, H. and Eulert, M (2014) The role of the information set for forecasting—with applications to risk management | 0.585 | 3 | 1 | 100% |
| 7 | Opschoor, A., van Dijk, D., and van der Wel, M (2017) Combining density forecasts using focused scoring rules | 0.511 | 2 | 1 | 100% |
| 8 | Ehm, W., Gneiting, T., Jordan, A., and Krüger, F (2016) Of quantiles and expectiles: consistent scoring functions, choquet representations and forecast rankings | 0.511 | 2 | 1 | 100% |
| 9 | Gneiting, T (2011) Making and evaluating point forecasts | 0.511 | 2 | 1 | 100% |
| 10 | Clements, M. and Harvey, D (2011) Combining probability forecasts | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 32 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | The Impact of Sampling Variability on Estimated Combinations of Distributional Forecasts | 1.000 | 5 | 3 |
| 2 | Loss-Based Variational Bayes Prediction | 0.644 | 2 | 2 |
| 3 | Solving the Forecast Combination Puzzle | 0.644 | 2 | 2 |
| 4 | Bayesian Forecasting in Economics and Finance: A Modern Review | 0.405 | 1 | 1 |