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Uncertainty, information, and disagreement of economic forecasters

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  • Mehdi Shoja
  • Ehsan S. Soofi

Abstract

An information framework is proposed for studying uncertainty and disagreement of economic forecasters. This framework builds upon the mixture model of combining density forecasts through a systematic application of the information theory. The framework encompasses the measures used in the literature and leads to their generalizations. The focal measure is the Jensen–Shannon divergence of the mixture which admits Kullback–Leibler and mutual information representations. Illustrations include exploring the dynamics of the individual and aggregate uncertainty about the US inflation rate using the survey of professional forecasters (SPF). We show that the normalized entropy index corrects some of the distortions caused by changes of the design of the SPF over time. Bayesian hierarchical models are used to examine the association of the inflation uncertainty with the anticipated inflation and the dispersion of point forecasts. Implementation of the information framework based on the variance and Dirichlet model for capturing uncertainty about the probability distribution of the economic variable are briefly discussed.

Suggested Citation

  • Mehdi Shoja & Ehsan S. Soofi, 2017. "Uncertainty, information, and disagreement of economic forecasters," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 796-817, October.
    • Handle: RePEc:taf:emetrv:v:36:y:2017:i:6-9:p:796-817
    DOI: 10.1080/07474938.2017.1307577
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    References listed on IDEAS

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    1. Bomberger, William A & Frazer, William J, Jr, 1981. "Interest Rates, Uncertainty and the Livingston Data," Journal of Finance, American Finance Association, vol. 36(3), pages 661-675, June.
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    Cited by:

    1. Bajgiran, Amirsaman H. & Mardikoraem, Mahsa & Soofi, Ehsan S., 2021. "Maximum entropy distributions with quantile information," European Journal of Operational Research, Elsevier, vol. 290(1), pages 196-209.
    2. Borgonovo, Emanuele & Hazen, Gordon B. & Jose, Victor Richmond R. & Plischke, Elmar, 2021. "Probabilistic sensitivity measures as information value," European Journal of Operational Research, Elsevier, vol. 289(2), pages 595-610.
    3. Lin, Boqiang & Li, Minyang, 2023. "Emerging Industry Development and Information Transmission in Financial Markets: Evidence from China's Renewable Energy," Energy Economics, Elsevier, vol. 128(C).
    4. Suh, Sangwon & Kim, Daehwan, 2021. "Inflation targeting and expectation anchoring: Evidence from developed and emerging market economies," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

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