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Practice Makes Perfect: Learning Effects with Household Point and Density Forecasts of Inflation

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  • Hana Braitsch
  • James Mitchell
  • Taylor Shiroff

Abstract

This paper shows how both the characteristics and the accuracy of the point and density forecasts from a well-known panel data survey of households' inflationary expectations – the New York Fed's Survey of Consumer Expectations – depend on the tenure of survey respondents. Households' point and density forecasts of inflation become significantly more accurate with repeated practice of completing the survey. These learning gains are best identified when tenure-based combination forecasts are constructed. Tenured households on average produce lower point forecasts of inflation, perceive less forecast uncertainty, round their uncertainty but not their point forecasts, report unimodal densities, and provide internally consistent point and density forecasts.

Suggested Citation

  • Hana Braitsch & James Mitchell & Taylor Shiroff, 2024. "Practice Makes Perfect: Learning Effects with Household Point and Density Forecasts of Inflation," Working Papers 24-25, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwq:99062
    DOI: 10.26509/frbc-wp-202425
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    References listed on IDEAS

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    1. Clements, Michael P., 2010. "Explanations of the inconsistencies in survey respondents' forecasts," European Economic Review, Elsevier, vol. 54(4), pages 536-549, May.
    2. Michael Weber & Francesco D'Acunto & Yuriy Gorodnichenko & Olivier Coibion, 2022. "The Subjective Inflation Expectations of Households and Firms: Measurement, Determinants, and Implications," Journal of Economic Perspectives, American Economic Association, vol. 36(3), pages 157-184, Summer.
    3. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    4. Carlos Madeira & Basit Zafar, 2015. "Heterogeneous Inflation Expectations and Learning," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(5), pages 867-896, August.
    5. Bomberger, William A, 1996. "Disagreement as a Measure of Uncertainty," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 28(3), pages 381-392, August.
    6. Alexander Glas & Matthias Hartmann, 2022. "Uncertainty measures from partially rounded probabilistic forecast surveys," Quantitative Economics, Econometric Society, vol. 13(3), pages 979-1022, July.
    7. Bharat Trehan, 2015. "Survey Measures of Expected Inflation and the Inflation Process," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(1), pages 207-222, February.
    8. Christopher D. Carroll, 2003. "Macroeconomic Expectations of Households and Professional Forecasters," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(1), pages 269-298.
    9. Kajal Lahiri & Xuguang Sheng, 2010. "Measuring forecast uncertainty by disagreement: The missing link," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 514-538.
    10. Binder, Carola C., 2017. "Measuring uncertainty based on rounding: New method and application to inflation expectations," Journal of Monetary Economics, Elsevier, vol. 90(C), pages 1-12.
    11. Roy Batchelor & Pami Dua, 1995. "Forecaster Diversity and the Benefits of Combining Forecasts," Management Science, INFORMS, vol. 41(1), pages 68-75, January.
    12. Gwangmin Kim & Carola Binder, 2023. "Learning-through-Survey in Inflation Expectations," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(2), pages 254-278, April.
    13. Krüger, Fabian & Pavlova, Lora, 2024. "Quantifying subjective uncertainty in survey expectations," International Journal of Forecasting, Elsevier, vol. 40(2), pages 796-810.
    14. Clements, Michael P., 2021. "Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 37(2), pages 634-646.
    15. 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.
    16. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    17. Binder, Carola Conces, 2015. "Whose expectations augment the Phillips curve?," Economics Letters, Elsevier, vol. 136(C), pages 35-38.
    18. Armantier, Olivier & Koşar, Gizem & Pomerantz, Rachel & Skandalis, Daphné & Smith, Kyle & Topa, Giorgio & van der Klaauw, Wilbert, 2021. "How economic crises affect inflation beliefs: Evidence from the Covid-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 443-469.
    19. Joseph Engelberg & Charles F. Manski & Jared Williams, 2011. "Assessing the temporal variation of macroeconomic forecasts by a panel of changing composition," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(7), pages 1059-1078, November.
    20. Gianna Boero & Jeremy Smith & Kenneth F. Wallis, 2015. "The Measurement and Characteristics of Professional Forecasters' Uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1029-1046, November.
    21. Kenneth F. Wallis, 2005. "Combining Density and Interval Forecasts: A Modest Proposal," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 983-994, December.
    22. Manski, Charles F. & Molinari, Francesca, 2010. "Rounding Probabilistic Expectations in Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 219-231.
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    More about this item

    Keywords

    inflation expectations; surveys; forecaster heterogeneity; combination forecasts; density forecasting; learning;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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