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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, 2018. "Households’ Inflation Perceptions and Expectations: Survey Evidence from New Zealand," MAGKS Papers on Economics 201805, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Kladivko, Kamil & Österholm, Pär, 2020. "Can Households Predict where the Macroeconomy is Headed?," Working Papers 2020:11, Örebro University, School of Business.
    3. 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).
    4. 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.
    5. Roedl, Marianne & Dupont, Genevieve, 2020. "Monetary policy implications of the COVID-19 outbreak, the social pandemic," MPRA Paper 99981, University Library of Munich, Germany.
    6. 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.
    7. Lake, A., 2020. "Optimal Feasible Expectations in Economics and Finance," Cambridge Working Papers in Economics 20105, Faculty of Economics, University of Cambridge.
    8. 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.).
    9. Oinonen, Sami & Vilmi, Lauri, 2021. "Analysing euro area inflation outlook with the Phillips curve," BoF Economics Review 5/2021, Bank of Finland.
    10. Karolina Tura-Gawron & Maria Siranova & Karol Fisikowski, 2018. "ARE CONSUMER INFLATION EXPECTATIONS AN INTERNATIONAL PHENOMENON? Results of spatial panel regressions models," GUT FME Working Paper Series A 50, Faculty of Management and Economics, Gdansk University of Technology.

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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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