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Heterogeneous inflation expectations and learning

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  • Madeira, Carlos

    () (Central Bank of Chile)

  • Zafar, Basit

    (Federal Reserve Bank of New York)

Abstract

Using the panel component of the Michigan Survey of Consumers, we estimate a learning model of inflation expectations, allowing for heterogeneous use of both private information and lifetime inflation experience. “Life-experience inflation” has a significant impact on individual expectations, but only for one-year-ahead inflation. Public information is substantially more relevant for longer-horizon expectations. Even controlling for life-experience inflation and public information, idiosyncratic information explains a nontrivial proportion of the inflation forecasts of agents. We find that women, ethnic minorities, and less educated agents—groups with perennially high inflation expectations—have a higher degree of heterogeneity in their idiosyncratic information and give less importance to recent movements in inflation. During the 1990s and early 2000s, consumers have believed inflation to be more persistent in the short term. However, quarterly inflation fluctuations have a smaller effect on long-term inflation expectations, especially in recent years, suggesting that agents believe shocks to be temporary.

Suggested Citation

  • Madeira, Carlos & Zafar, Basit, 2012. "Heterogeneous inflation expectations and learning," Staff Reports 536, Federal Reserve Bank of New York, revised 01 Jun 2014.
  • Handle: RePEc:fip:fednsr:536
    Note: Previous title: “Heterogeneous Inflation Expectations, Learning, and Market Outcomes” For a published version of this report, see Carlos Madeira and Basit Zafar, "Heterogeneous Inflation Expectations and Learning," Journal of Money, Credit and Banking 47, no. 5 (August 2015): 867-96.
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Alexander Ballantyne & Christian Gillitzer & David Jacobs & Ewan Rankin, 2016. "Disagreement about Inflation Expectations," RBA Research Discussion Papers rdp2016-02, Reserve Bank of Australia.
    2. Kuchler, Theresa & Zafar, Basit, 2015. "Personal experiences and expectations about aggregate outcomes," Staff Reports 748, Federal Reserve Bank of New York.
    3. Armona, Luis & Fuster, Andreas & Zafar, Basit, 2016. "Home price expectations and behavior: evidence from a randomized information experiment," Staff Reports 798, Federal Reserve Bank of New York.
    4. Carvalho, Carlos & Nechio, Fernanda, 2014. "Do people understand monetary policy?," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 108-123.
    5. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2017. "Inflation Expectations, Learning, and Supermarket Prices: Evidence from Survey Experiments," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(3), pages 1-35, July.
    6. Olivier Armantier & Scott Nelson & Giorgio Topa & Wilbert Van der Klaauw & Basit Zafar, 2012. "The price is right: updating of inflation expectations in a randomized price information experiment," Staff Reports 543, Federal Reserve Bank of New York.
    7. repec:eee:ecolec:v:138:y:2017:i:c:p:56-63 is not listed on IDEAS
    8. Christa Hainz & Nikolay Hristov, 2017. "Zur Kredithürde: Perzeption der Kreditvergabebereitschaft der Banken und unternehmensspezifische Kreditmarkterfahrung," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 70(07), pages 51-54, April.
    9. Xu, Yingying & Chang, Hsu-Ling & Lobonţ, Oana-Ramona & Su, Chi-Wei, 2016. "Modeling heterogeneous inflation expectations: empirical evidence from demographic data?," Economic Modelling, Elsevier, vol. 57(C), pages 153-163.

    More about this item

    Keywords

    inflation expectations; imperfect information; heterogeneous expectations; learning; sticky information;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • 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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