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A Benchmark Model for Fixed-Target Arctic Sea Ice Forecasting

Author

Listed:
  • Francis X. Diebold

    (University of Pennsylvania)

  • Maximilian Gobel

    (University of Lisbon)

Abstract

We propose a reduced-form benchmark predictive model (BPM) for ?xed-target forecasting of Arctic sea ice extent, and we provide a case study of its real-time performance for target date September 2020. We visually detail the evolution of the statistically-optimal point, interval, and density forecasts as time passes, new information arrives, and the end of September approaches. Comparison to the BPM may prove useful for evaluating and selecting among various more sophisticated dynamical sea ice models, which are widely used to quantify the likely future evolution of Arctic conditions and their two-way interaction with economic activity.

Suggested Citation

  • Francis X. Diebold & Maximilian Gobel, 2022. "A Benchmark Model for Fixed-Target Arctic Sea Ice Forecasting," PIER Working Paper Archive 22-002, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:22-002
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    Cited by:

    1. Brock, William A. & Miller, J. Isaac, 2024. "Polar amplification in a moist energy balance model: A structural econometric approach to estimation and testing," Journal of Econometrics, Elsevier, vol. 245(1).
    2. Diebold, Francis X. & Göbel, Maximilian & Goulet Coulombe, Philippe, 2023. "Assessing and comparing fixed-target forecasts of Arctic sea ice: Glide charts for feature-engineered linear regression and machine learning models," Energy Economics, Elsevier, vol. 124(C).

    More about this item

    Keywords

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    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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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