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Differential Interpretation in the Survey of Professional Forecasters

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  • SEBASTIANO MANZAN

Abstract

In this paper, I estimate a simple Bayesian learning model to expectations data from the Survey of Professional Forecasters. I reformulate the model in terms of forecast revisions, which allows one to abstract from differences in priors and to focus the analysis on the relationship between news and revisions. The empirical analysis shows that there is significant heterogeneity in the interpretation of news among forecasters, in particular at longer horizons, while it decreases closer to the forecast target date. The results also indicate a positive relationship between prior sentiment and interpretation of the signal, in the sense that relatively optimistic (pessimistic) forecasters are likely to believe that the signal under (over) estimates the future realization and assign it a low (high) weight in the forecast revision.

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  • Sebastiano Manzan, 2011. "Differential Interpretation in the Survey of Professional Forecasters," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(5), pages 993-1017, August.
    • Handle: RePEc:wly:jmoncb:v:43:y:2011:i:5:p:993-1017
    DOI: 10.1111/j.1538-4616.2011.00404.x
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    2. Chini, Emilio Zanetti, 2023. "Can we estimate macroforecasters’ mis-behavior?," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    3. Michael P. Clements, 2022. "Individual forecaster perceptions of the persistence of shocks to GDP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 640-656, April.
    4. Manzan, Sebastiano, 2021. "Are professional forecasters Bayesian?," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    5. Roccazzella, Francesco & Candelon, Bertrand, 2022. "Should we care about ECB inflation expectations?," LIDAM Discussion Papers LFIN 2022004, Université catholique de Louvain, Louvain Finance (LFIN).
    6. Sinha, Rajesh Kumar, 2021. "Macro disagreement and analyst forecast properties," Journal of Contemporary Accounting and Economics, Elsevier, vol. 17(1).
    7. Karlyn Mitchell & Douglas K. Pearce, 2017. "Direct Evidence on Sticky Information from the Revision Behavior of Professional Forecasters," Southern Economic Journal, John Wiley & Sons, vol. 84(2), pages 637-653, October.
    8. Li, You & Tay, Anthony, 2021. "The role of macroeconomic and policy uncertainty in density forecast dispersion," Journal of Macroeconomics, Elsevier, vol. 67(C).
    9. Giulia Piccillo & Poramapa Poonpakdee, 2021. "Effects of Macro Uncertainty on Mean Expectation and Subjective Uncertainty: Evidence from Households and Professional Forecasters," CESifo Working Paper Series 9486, CESifo.

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