Francis X. Diebold, Maximilian Goebel, Philippe Goulet Coulombe
arXiv 21 Jun 2022 · Econometrics · publishedEnergy Economics (2023) · 15 citations (OpenAlex)
arXiv:2206.10721 · PDF · DOI · OpenAlex · Extracted main text
We use "glide charts" (plots of sequences of root mean squared forecast errors as the target date is approached) to evaluate and compare fixed-target forecasts of Arctic sea ice. We first use them to evaluate the simple feature-engineered linear regression (FELR) forecasts of Diebold and Goebel (2021), and to compare FELR forecasts to naive pure-trend benchmark forecasts. Then we introduce a much more sophisticated feature-engineered machine learning (FEML) model, and we use glide charts to evaluate FEML forecasts and compare them to a FELR benchmark. Our substantive results include the frequent appearance of predictability thresholds, which differ across months, meaning that accuracy initially fails to improve as the target date is approached but then increases progressively once a threshold lead time is crossed. Also, we find that FEML can improve appreciably over FELR when forecasting "turning point" months in the annual cycle at horizons of one to three months ahead.
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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 and Göbel (2022) A Benchmark Model for Fixed-Target Arctic Sea Ice Forecasting, Economics Letters\/, 215, 110478 | 1.000 | 6 | 4 | 100% |
| 2 | Goulet Coulombe and Göbel (2021) Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis, Journal of Climate\/, 34, 5523–5541 | 0.928 | 4 | 3 | 100% |
| MRF | unmatched citation key MRF | 0.874 | 7 | 2 | 100% |
| 4 | Diebold and Rudebusch (2022) Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections, Journal of Econometrics\/, 2… | 0.737 | 3 | 2 | 100% |
| 5 | Bhatt, Meier, Blanchard-Wrigglesworth, Massonnet, Goessling, V., Bie… (2022) Sea Ice Outlook: 2022 Post Season Report | 0.737 | 3 | 2 | 100% |
| 6 | Andersson, Hosking, Pérez-Ortiz, Paige, Elliott, Russell, Law, Jones… (2021) Seasonal Arctic Sea Ice Forecasting with Probabilistic Deep Learning, Nature Communications\/, 12, 1–12 | 0.737 | 3 | 2 | 100% |
| 7 | Bushuk, Msadek, Winton, Vecchi, Yang, Rosati, and Gudgel (2019) Regional Arctic Sea–Ice Prediction: Potential versus Operational Seasonal Forecast Skill, Climate Dynamics\/, 52, 2721–2743 | 0.511 | 2 | 1 | 100% |
| 8 | Day, Tietsche, and Hawkins (2014) Pan-Arctic and Regional Sea Ice Predictability: Initialization Month Dependence, Journal of Climate\/, 27, 4371–4390 | 0.511 | 2 | 1 | 100% |
| TBTP | unmatched citation key TBTP | 0.511 | 2 | 1 | 100% |
| 10 | Breiman (2001) Random Forests, Machine learning\/, 45, 5–32 | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 27 scored citations. 2 of these could not be matched to a bibliography entry, so only the citation key is shown.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | What Does it Take to Control Global Temperatures? A toolbox for testing and estimating the impact of economic policies on climate | 0.405 | 1 | 1 |