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Heterogeneity and learning in inflation expectation formation: an empirical assessment

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  • Emiliano Santoro
  • Damjan Pfajfar

Abstract

Relying on Michigan Survey's monthly micro data on inflation expectations we try to determine the main features - in terms of sources and degree of heterogeneity - of inflation expectation formation over different phases of the business cycle. Different learning rules have been applied to the data, in order to test whether agents are learning and whether their expectations are converging towards perfect foresight. Results suggest that behaviour of agents in the right hand side of the distribution is more associated with learning dynamics. Tests for "static" and "dynamic" versions of sticky information are also conducted. Only agents in the middle of the distribution are regularly updating their information sets. Evidence of rational inattention has been found for agents comprised in the upper end of the distribution. We identify three regions of the overall distribution corresponding to different expectation formation processes, which display a heterogeneous response to main macroeconomic indicators : a static or highly autoregressive (LHS) group, a "nearly" rational group (middle), and a group of agents (RHS) behaving in accordance to adaptive learning and sticky information. The latter, generally speaking, are too "pessimistic" as they overreact to macroeconomic fluctuations.

Suggested Citation

  • Emiliano Santoro & Damjan Pfajfar, 2006. "Heterogeneity and learning in inflation expectation formation: an empirical assessment," Department of Economics Working Papers 0607, Department of Economics, University of Trento, Italia.
    • Handle: RePEc:trn:utwpde:0607
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    Cited by:

    1. David G. Blanchflower & Conall MacCoille, 2009. "The formation of inflation expectations: an empirical analysis for the UK," NBER Working Papers 15388, National Bureau of Economic Research, Inc.
    2. Ghazanchyan, Manuk, 2014. "Unraveling the Monetary Policy Transmission Mechanism in Sri Lanka," MPRA Paper 59444, University Library of Munich, Germany.
    3. Anufriev, M. & Assenza, T. & Hommes, C.H. & Massaro, D., 2008. "Interest Rate Rules with Heterogeneous Expectations," CeNDEF Working Papers 08-08, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    4. Honkapohja, Seppo & Evans, George W., 2011. "Learning as a Rational Foundation for Macroeconomics and Finance," CEPR Discussion Papers 8340, C.E.P.R. Discussion Papers.
    5. Pfajfar, D. & Santoro, E., 2008. "Asymmetries in Inflation Expectation Formation Across Demographic Groups," Cambridge Working Papers in Economics 0824, Faculty of Economics, University of Cambridge.
    6. Steffen Henzel, 2008. "Learning Trend Inflation – Can Signal Extraction Explain Survey Forecasts?," ifo Working Paper Series 55, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    7. Balazs VARGA & Zsolt DARVAS, 2010. "Time-Varying Coefficient Methods to Measure Inflation Persistence," EcoMod2010 259600167, EcoMod.
    8. Anufriev, Mikhail & Assenza, Tiziana & Hommes, Cars & Massaro, Domenico, 2013. "Interest Rate Rules And Macroeconomic Stability Under Heterogeneous Expectations," Macroeconomic Dynamics, Cambridge University Press, vol. 17(8), pages 1574-1604, December.

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    More about this item

    Keywords

    Heterogeneous Expectations; Adaptive Learning; Sticky Information; Survey Expectations;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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