Matteo Iacopini, Francesco Ravazzolo, Luca Rossini
arXiv 19 Jun 2020 · Statistics — Methodology
arXiv:2006.11265 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It extends the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable's range. A test is also introduced to statistically compare the predictive ability of different forecasts. The ACPS is of general use in any situation where the decision maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry in the ACPS are illustrated. Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets (US employment growth) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period.
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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 R. Ranjan (2011) Comparing density forecasts using threshold- and quantile-weighted scoring rules | 0.843 | 3 | 3 | 100% |
| 2 | Winkler, R. L (1994) Evaluating probabilities: Asymmetric scoring rules | 0.737 | 3 | 2 | 100% |
| 3 | Christoffersen, P. F. and F. X. Diebold (1996) Further results on forecasting and model selection under asymmetric loss | 0.644 | 2 | 2 | 100% |
| 4 | Elliott, G., A. Timmermann, and I. Komunjer (2005) Estimation and testing of forecast rationality under flexible loss | 0.644 | 2 | 2 | 100% |
| 5 | Patton, A. J. and A. Timmermann (2007) Testing forecast optimality under unknown loss | 0.644 | 2 | 2 | 100% |
| 6 | Demetrescu, M. and S. H. Hoke (2019) Predictive regressions under asymmetric loss: Factor augmentation and model selection | 0.644 | 2 | 2 | 100% |
| 7 | Gneiting, T. and A. E. Raftery (2007) Strictly proper scoring rules, prediction, and estimation | 0.644 | 2 | 2 | 100% |
| 8 | Matheson, J. E. and R. L. Winkler (1976) Scoring rules for continuous probability distributions | 0.585 | 3 | 1 | 100% |
| 9 | Gianfreda, A., F. Ravazzolo, and L. Rossini (2020) Large time-varying volatility models for electricity prices | 0.511 | 2 | 1 | 100% |
| 10 | Gneiting, T (2011) Making and evaluating point forecasts | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 45 scored citations.