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Understanding Survey Based Inflation Expectations

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Abstract

Survey based measures of inflation expectations are not informationally efficient yet carry important information about future inflation. This paper explores the economic significance of informational inefficiencies of survey expectations. A model selection algorithm is applied to the inflation expectations of households and professionals using a large panel of macroeconomic data. The expectations of professionals are best described by different indicators than the expectations of households. A forecast experiment finds that it is difficult to exploit informational inefficiencies to improve inflation forecasts, suggesting that the economic cost of the surveys' deviation from rationality is not large.

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  • Travis J. Berge, 2017. "Understanding Survey Based Inflation Expectations," Finance and Economics Discussion Series 2017-046, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2017-46
    DOI: 10.17016/FEDS.2017.046
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    Cited by:

    1. Bernd Hayo & Florian Neumeier, 2022. "Households’ inflation perceptions and expectations: survey evidence from New Zealand," International Economics and Economic Policy, Springer, vol. 19(1), pages 185-217, February.
    2. Leilane de Freitas Rocha Cambara & Roberto Meurer, Gilberto Tadeu Lima, 2019. "Deviating from Perfect Foresight but not from Theoretical Consistency: The Behavior of Inflation Expectations in Brazil," Working Papers, Department of Economics 2019_36, University of São Paulo (FEA-USP).
    3. Randal J. Verbrugge & Saeed Zaman, 2021. "Whose Inflation Expectations Best Predict Inflation?," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(19), pages 1-7, October.
    4. Kladivko, Kamil & Österholm, Pär, 2020. "Can Households Predict where the Macroeconomy is Headed?," Working Papers 2020:11, Örebro University, School of Business.
    5. Christina Anderl & Guglielmo Maria Caporale, 2023. "Forecasting inflation with a zero lower bound or negative interest rates: Evidence from point and density forecasts," Manchester School, University of Manchester, vol. 91(3), pages 171-232, June.
    6. Tomasz Łyziak & Xuguang Simon Sheng, 2023. "Disagreement in Consumer Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(8), pages 2215-2241, December.
    7. Mayukh Dass & Masoud Moradi & Fereshteh Zihagh, 2023. "Forecasting purchase rates of new products introduced in existing categories," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 385-408, September.
    8. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. Pooja Kapoor & Sujata Kar, 2024. "Do Central Bank Communications Influence Survey of Professional Forecasters? An Empirical Investigation," Business Perspectives and Research, , vol. 12(1), pages 100-112, January.
    10. Lake, A., 2020. "Optimal Feasible Expectations in Economics and Finance," Cambridge Working Papers in Economics 20105, Faculty of Economics, University of Cambridge.
    11. Cambara, Leilane de Freitas Rocha & Meurer, Roberto & Lima, Gilberto Tadeu, 2022. "Deviating from full rationality but not from theoretical consistency: The behavior of inflation expectations in Brazil," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 492-501.
    12. Chris Campos & Michael McMain & Mathieu Pedemonte, 2022. "Understanding Which Prices Affect Inflation Expectations," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2022(06), pages 1-7, April.
    13. Alessandro Barbarino & Travis J. Berge & Han Chen & Andrea Stella, 2020. "Which Output Gap Estimates Are Stable in Real Time and Why?," Finance and Economics Discussion Series 2020-102, Board of Governors of the Federal Reserve System (U.S.).
    14. Oinonen, Sami & Vilmi, Lauri, 2021. "Analysing euro area inflation outlook with the Phillips curve," BoF Economics Review 5/2021, Bank of Finland.
    15. Roedl, Marianne & Dupont, Genevieve, 2020. "Monetary policy implications of the COVID-19 outbreak, the social pandemic," MPRA Paper 99981, University Library of Munich, Germany.
    16. Christian Pierdzioch & Rangan Gupta & Hossein Hassani & Emmanuel Silva, 2018. "Forecasting Changes of Economic Inequality: A Boosting Approach," Working Papers 201868, University of Pretoria, Department of Economics.
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    More about this item

    Keywords

    Informational efficiency; Phillips curve; Survey based inflation expectations; Boosting; Inflation forecasting; Machine learning;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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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