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Quantifying Subjective Uncertainty in Survey Expectations

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  • Krüger, Fabian
  • Pavlova, Lora

Abstract

Several recent surveys ask for a person's subjective probabilities that the inflation rate falls into various outcome ranges. We provide a new measure of the uncertainty implicit in such probabilities. The measure has several advantages over existing methods: It is robust, trivial to implement, requires no functional form assumptions, and is well-defined for all logically possible probabilities. These advantages are particularly relevant when analyzing large scale consumer surveys. We illustrate the new measure using data from the Survey of Consumer Expectations.

Suggested Citation

  • Krüger, Fabian & Pavlova, Lora, 2020. "Quantifying Subjective Uncertainty in Survey Expectations," Working Papers 14, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    • Handle: RePEc:zbw:pp1859:14
    DOI: 10.18452/21401
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    1. Olivier Coibion & Yuriy Gorodnichenko & Saten Kumar, 2018. "How Do Firms Form Their Expectations? New Survey Evidence," American Economic Review, American Economic Association, vol. 108(9), pages 2671-2713, September.
    2. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
    3. James E. Matheson & Robert L. Winkler, 1976. "Scoring Rules for Continuous Probability Distributions," Management Science, INFORMS, vol. 22(10), pages 1087-1096, June.
    4. Werner Ehm & Tilmann Gneiting & Alexander Jordan & Fabian Krüger, 2016. "Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 505-562, June.
    5. Andrew Schotter & Isabel Trevino, 2014. "Belief Elicitation in the Laboratory," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 103-128, August.
    6. Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F., 2011. "Scoring rules and survey density forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 379-393.
    7. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    8. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    9. Olivier Armantier & Giorgio Topa & Wilbert Van der Klaauw & Basit Zafar, 2017. "An overview of the Survey of Consumer Expectations," Economic Policy Review, Federal Reserve Bank of New York, issue 23-2, pages 51-72.
    10. Marc-André Gosselin & Mikael Khan, 2015. "A Survey of Consumer Expectations for Canada," Bank of Canada Review, Bank of Canada, vol. 2015(Autumn), pages 14-23.
    11. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    12. Michael P. Clements, 2014. "Forecast Uncertainty- Ex Ante and Ex Post : U.S. Inflation and Output Growth," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 206-216, April.
    13. Adeline Delavande, 2014. "Probabilistic Expectations in Developing Countries," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 1-20, August.
    14. Robert Rich & Joseph Tracy, 2010. "The Relationships among Expected Inflation, Disagreement, and Uncertainty: Evidence from Matched Point and Density Forecasts," The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 200-207, February.
    15. Andrew Caplin & Mark Dean & John Leahy, 2022. "Rationally Inattentive Behavior: Characterizing and Generalizing Shannon Entropy," Journal of Political Economy, University of Chicago Press, vol. 130(6), pages 1676-1715.
    16. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    17. Reinhard Selten, 1998. "Axiomatic Characterization of the Quadratic Scoring Rule," Experimental Economics, Springer;Economic Science Association, vol. 1(1), pages 43-61, June.
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    Cited by:

    1. Peter Backé & Elisabeth Beckmann, 2020. "What drives people’s expectations of euro adoption? – Evidence from the OeNB Euro Survey on selected CESEE countries," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/20, pages 57-79.
    2. Conrad, Christian & Enders, Zeno & Glas, Alexander, 2022. "The role of information and experience for households’ inflation expectations," European Economic Review, Elsevier, vol. 143(C).
    3. Dovern, Jonas, 2024. "Eliciting expectation uncertainty from private households," International Journal of Forecasting, Elsevier, vol. 40(1), pages 113-123.
    4. Montserrat Guillen & Miguel Santolino & Xenxo Vidal-Llana, 2022. ""Inequality of subjective economic uncertainty and individual economic prospects in the pandemic period"," IREA Working Papers 202202, University of Barcelona, Research Institute of Applied Economics, revised Feb 2022.

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