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Information flows and disagreement

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  • Badarinza, Cristian
  • Gross, Marco

Abstract

The aim of this study is to assess the extent to which the degree of heterogeneity of inflation expectations is driven by the flow of information related to current and future price developments. To that end, we follow three routes: i) We propose different measures of information flow that have either a sender or a receiver perspective; ii) We present empirical results for the US and selected EU countries that aim to corroborate the hypothesis that news have the ability to densify expectations, i.e. to reduce forecast heterogeneity; and iii) We augment some otherwise standard models of expectation formation by allowing the individual updating frequency to depend on the observed measure of information flow; since the updating frequency is higher at times of high inflation and decreasing thereafter, this mechanism can contribute to upward biases in inflation expectations over long periods of time. JEL Classification: D12, D84, E31, E37

Suggested Citation

  • Badarinza, Cristian & Gross, Marco, 2012. "Information flows and disagreement," Working Paper Series 1475, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20121475
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    File URL: https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1475.pdf
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    References listed on IDEAS

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    1. Reis, Ricardo, 2006. "Inattentive consumers," Journal of Monetary Economics, Elsevier, vol. 53(8), pages 1761-1800, November.
    2. Thomas A. Eife & W. Timothy Coombs, 2006. "Coping with People’s Inflation Perceptions During a Currency Changeover," Working Papers 0435, University of Heidelberg, Department of Economics, revised Dec 2006.
    3. N. Gregory Mankiw & Ricardo Reis, 2002. "Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," The Quarterly Journal of Economics, Oxford University Press, vol. 117(4), pages 1295-1328.
    4. Michael Ehrmann & Sylvester Eijffinger & Marcel Fratzscher, 2012. "The Role of Central Bank Transparency for Guiding Private Sector Forecasts," Scandinavian Journal of Economics, Wiley Blackwell, pages 1018-1052.
    5. Jonas Dovern & Ulrich Fritsche & Jiri Slacalek, 2012. "Disagreement Among Forecasters in G7 Countries," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1081-1096, November.
    6. Christopher D. Carroll, 2003. "Macroeconomic Expectations of Households and Professional Forecasters," The Quarterly Journal of Economics, Oxford University Press, pages 269-298.
    7. Michael J. Lamla & Thomas Maag, 2012. "The Role of Media for Inflation Forecast Disagreement of Households and Professional Forecasters," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(7), pages 1325-1350, October.
    8. Stephen Morris & Hyun Song Shin, 2002. "Social Value of Public Information," American Economic Review, American Economic Association, pages 1521-1534.
    9. Brachinger, Hans Wolfgang, 2006. "Euro or “Teuro”?: The Euro-induced Perceived Inflation in Germany," DQE Working Papers 5, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland.
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    Cited by:

    1. Lena Draeger & Michael J. Lamla, 2015. "Disagreement à la Taylor: Evidence from Survey Microdata," KOF Working papers 15-380, KOF Swiss Economic Institute, ETH Zurich.

    More about this item

    Keywords

    disagreement; Heterogeneous beliefs; public information;

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • 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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