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Testing the Value of Probability Forecasts for Calibrated Combining

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  • Kajal Lahiri
  • Huaming Peng
  • Yongchen Zhao

Abstract

We combine the probability forecasts of real GDP declines from the U.S. Survey of Professional Forecasters, after trimming the forecasts that do not have "value" in the sense of Merton (1981). For this purpose, we propose a new test to evaluate probability forecasts that does not require converting the probabilities to binary forecasts before testing. The test accommodates serial correlation and skewness in the forecasts, and is implemented using a circular block bootstrap procedure. We find that the number of forecasters making valuable forecasts decreases sharply as horizon increases. The beta-transformed linear pool, based only on the valuable individual forecasts, is shown to outperform the simple average for all horizons on a number of performance measures including calibration and sharpness.

Suggested Citation

  • Kajal Lahiri & Huaming Peng & Yongchen Zhao, 2013. "Testing the Value of Probability Forecasts for Calibrated Combining," Discussion Papers 13-02, University at Albany, SUNY, Department of Economics.
  • Handle: RePEc:nya:albaec:13-02
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    Cited by:

    1. Constantin Bürgi & Tara M. Sinclair, 2017. "A nonparametric approach to identifying a subset of forecasters that outperforms the simple average," Empirical Economics, Springer, vol. 53(1), pages 101-115, August.
    2. Graham Elliott, 2017. "Forecast combination when outcomes are difficult to predict," Empirical Economics, Springer, vol. 53(1), pages 7-20, August.
    3. Yongchen Zhao, 2020. "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 77-97, November.

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