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Disagreement à la Taylor: Evidence from Survey Microdata

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  • Lena Draeger
  • Michael J. Lamla

Abstract

There is a growing interest in studying the disagreement of economic agents. Most studies, however, focus on the disagreement regarding one specific variable, hereby neglecting that disagreement may be comoving with disagreement on other variables. In this paper we explore to which extent disagreement regarding the interest rate is driven by disagreement on inflation and on unemployment. This relationship can be motivated by the existence of the Taylor rule. Using micro survey data for both professional forecasters and consumers, we provide evidence that disagreement on the future interest rate is mainly driven by disagreement on inflation. Exploring further determinants, we confirm that central bank transparency as well as news on money and credit conditions significantly influence disagreement.

Suggested Citation

  • 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.
    • Handle: RePEc:kof:wpskof:15-380
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    File URL: http://dx.doi.org/10.3929/ethz-a-010427317
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    References listed on IDEAS

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    1. Dräger, Lena & Lamla, Michael J., 2012. "Updating inflation expectations: Evidence from micro-data," Economics Letters, Elsevier, vol. 117(3), pages 807-810.
    2. 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.
    3. N. Gregory Mankiw & Ricardo Reis & Justin Wolfers, 2004. "Disagreement about Inflation Expectations," NBER Chapters,in: NBER Macroeconomics Annual 2003, Volume 18, pages 209-270 National Bureau of Economic Research, Inc.
    4. Mark Gertler & Jordi Gali & Richard Clarida, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1661-1707, December.
    5. Andrade, Philippe & Crump, Richard K. & Eusepi, Stefano & Moench, Emanuel, 2016. "Fundamental disagreement," Journal of Monetary Economics, Elsevier, vol. 83(C), pages 106-128.
    6. 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.
    7. Carvalho, Carlos & Nechio, Fernanda, 2014. "Do people understand monetary policy?," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 108-123.
    8. 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.
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    12. Frieder Mokinski & Xuguang (Simon) Sheng & Jingyun Yang, 2015. "Measuring Disagreement in Qualitative Expectations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(5), pages 405-426, August.
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    Cited by:

    1. Dovern, Jonas, 2015. "A multivariate analysis of forecast disagreement: Confronting models of disagreement with survey data," European Economic Review, Elsevier, vol. 80(C), pages 16-35.

    More about this item

    Keywords

    Disagreement; Taylor rule; Interest rate expectations; Inflation expectations; Unemployment expectations; Microdata;

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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