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Testing the Value of Probability Forecasts for Calibrated Combining.

Kajal Lahiri1, Huaming Peng1, Yongchen Zhao1

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Summary

This study identifies valuable real GDP decline probability forecasts from professional forecasters. Combining these valuable forecasts improves accuracy compared to simple averages, especially at longer horizons.

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Area of Science:

  • Economics
  • Econometrics
  • Forecasting Science

Background:

  • Assessing the accuracy of economic forecasts is crucial for policy and investment decisions.
  • Probability forecasts of real Gross Domestic Product (GDP) decline are key indicators.
  • Existing methods for evaluating probability forecasts have limitations.

Purpose of the Study:

  • To develop and apply a novel test for evaluating probability forecasts of real GDP decline.
  • To identify and combine 'valuable' individual forecasts using the Kuiper Skill Score.
  • To assess the performance of combined forecasts against simple averages.

Main Methods:

  • Utilized the U.S. Survey of Professional Forecasters data.
  • Developed a new test for probability forecasts accommodating serial correlation and skewness.
  • Applied the Kuiper Skill Score to identify valuable forecasts (Merton, 1981).
  • Employed a beta-transformed linear pool combination scheme.

Main Results:

  • The number of forecasters providing valuable probability forecasts significantly decreases with increasing forecast horizon.
  • The beta-transformed linear pool combination scheme consistently outperformed the simple average across various performance metrics.
  • The proposed test effectively identifies valuable forecasters ex ante.

Conclusions:

  • A new method for evaluating probability forecasts enhances the identification of skilled forecasters.
  • Combining valuable individual forecasts leads to more accurate and reliable combined forecasts.
  • The findings have implications for improving economic forecasting accuracy and decision-making.