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Understanding Survey Based Inflation Expectations

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  • Travis J. Berge

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.

Suggested Citation

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

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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. 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.
    3. 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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    Keywords

    Informational efficiency ; Phillips curve ; Survey based inflation expectations ; Boosting ; Inflation forecasting ; Machine learning;

    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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