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Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice:Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models

Author

Listed:
  • Francis X. Diebold

    (University of Pennsylvania)

  • Maximilian Gobel

    (University of Lisbon)

  • Philippe Goulet Coulombe

    (University of Quebec)

Abstract

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 Gobel (2022), 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.

Suggested Citation

  • Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe, 2022. "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice:Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," PIER Working Paper Archive 22-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:22-028
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    References listed on IDEAS

    as
    1. Diebold, Francis X. & Rudebusch, Glenn D., 2022. "Probability assessments of an ice-free Arctic: Comparing statistical and climate model projections," Journal of Econometrics, Elsevier, vol. 231(2), pages 520-534.
    2. Philippe Goulet Coulombe & Maximilian Gobel, 2020. "Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis," Papers 2005.02535, arXiv.org, revised Mar 2021.
    3. Eddy Bekkers & Joseph F. Francois & Hugo Rojas†Romagosa, 2018. "Melting Ice Caps and the Economic Impact of Opening the Northern Sea Route," Economic Journal, Royal Economic Society, vol. 128(610), pages 1095-1127, May.
    4. Ing, Ching-Kang, 2003. "Multistep Prediction In Autoregressive Processes," Econometric Theory, Cambridge University Press, vol. 19(2), pages 254-279, April.
    5. Diebold, Francis X. & Göbel, Maximilian, 2022. "A benchmark model for fixed-target Arctic sea ice forecasting," Economics Letters, Elsevier, vol. 215(C).
    6. Tom R. Andersson & J. Scott Hosking & María Pérez-Ortiz & Brooks Paige & Andrew Elliott & Chris Russell & Stephen Law & Daniel C. Jones & Jeremy Wilkinson & Tony Phillips & James Byrne & Steffen Tiets, 2021. "Seasonal Arctic sea ice forecasting with probabilistic deep learning," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Seasonal climate forecasting; forecast evaluation and comparison; prediction;
    All these keywords.

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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