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Measuring disagreement in probabilistic and density forecasts

Author

Listed:
  • Ryan Cumings-Menon
  • Minchul Shin
  • Keith Sill

Abstract

In this paper, we introduce and study a class of disagreement measures for probability distribution forecasts based on the Wasserstein metric. We describe a few advantageous properties of this measure of disagreement between forecasters. After describing alternatives to our proposal, we use examples to compare these measures to one another in closed form. We provide two empirical illustrations. The first application uses our measure to gauge disagreement among professional forecasters about output growth and inflation rate in the Eurozone. The second application employs our measure to gauge disagreement among multivariate predictive distributions generated by different forecasting methods.

Suggested Citation

  • Ryan Cumings-Menon & Minchul Shin & Keith Sill, 2020. "Measuring disagreement in probabilistic and density forecasts," Working Papers 21-03, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpwp:89578
    DOI: 10.21799/frbp.wp.2021.03
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