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Are Some Forecasters Really Better than Others? A Note

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  • ULRICH HOUNYO
  • KAJAL LAHIRI

Abstract

In this paper, we use bootstrap approach to test the null hypothesis that all forecasters in the U.S. Surveys of Professional Forecasters (SPF) have equal ability. Our bootstrap procedure captures any potential cross‐sectional and serial correlation in the forecast errors while preserving the unbalanced nature of the panel data. Once we account for the presence of cross‐sectional and serial correlation in the forecast errors while resampling, we find convincing evidence that some individuals really are better than others—this is in sharp contrast to the findings of D'Agostino et al. (2012).

Suggested Citation

  • Ulrich Hounyo & Kajal Lahiri, 2023. "Are Some Forecasters Really Better than Others? A Note," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(2-3), pages 577-593, March.
    • Handle: RePEc:wly:jmoncb:v:55:y:2023:i:2-3:p:577-593
    DOI: 10.1111/jmcb.12925
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    References listed on IDEAS

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    1. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    2. Shao, Xiaofeng, 2010. "The Dependent Wild Bootstrap," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 218-235.
    3. Michael P. Clements, 2020. "Are Some Forecasters’ Probability Assessments of Macro Variables Better Than Those of Others?," Econometrics, MDPI, vol. 8(2), pages 1-16, May.
    4. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    5. Kajal Lahiri & Huaming Peng & Yongchen Zhao, 2017. "Online learning and forecast combination in unbalanced panels," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 257-288, March.
    6. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    7. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.
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