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Heterogeneous expectations, learning and European inflation dynamics

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  • Weber, Anke

Abstract

This paper is the first attempt to investigate the performance of different learning rules in fitting survey data of household and expert inflation expectations in five core European economies (France, Germany, Italy, Netherlands and Spain). Overall it is found that constant gain learning performs well in out-of-sample forecasting. It is also shown that households in high inflation countries are using higher best fitting constant gain parameters than those in low inflation countries. They are hence able to pick up structural changes faster. Professional forecasters update their information sets more frequently than households. Furthermore, household expectations in the Euro Area have not converged to the inflation goal of the ECB, which is to keep inflation below to but close to 2% in the medium run. This contrasts the findings for professional experts, which seem to be more inclined to incorporate the implications of monetary union for the convergence in inflation rates into their expectations. --

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Bibliographic Info

Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 1: Economic Studies with number 2007,16.

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Date of creation: 2007
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Handle: RePEc:zbw:bubdp1:6137

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Keywords: Monetary policy; heterogeneous expectations; adaptive learning; survey expectations;

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References

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  1. Albert Marcet & Juan P. Nicolini, 1995. "Recurrent hyperinflations and learning," Economics Working Papers 244, Department of Economics and Business, Universitat Pompeu Fabra, revised Nov 2001.
  2. Fabio Milani, 2005. "Expectations, Learning and Macroeconomic Persistence," Working Papers 050608, University of California-Irvine, Department of Economics.
  3. Mankiw, N. Gregory & Reis, Ricardo, 2007. "Sticky Information in General Equilibrium," Scholarly Articles 3415323, Harvard University Department of Economics.
  4. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
  5. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
  6. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  7. Holden, K & Peel, D A, 1990. "On Testing for Unbiasedness and Efficiency of Forecasts," The Manchester School of Economic & Social Studies, University of Manchester, vol. 58(2), pages 120-27, June.
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Cited by:
  1. Steffen Henzel, 2008. "Learning Trend Inflation – Can Signal Extraction Explain Survey Forecasts?," Ifo Working Paper Series Ifo Working Paper No. 55, Ifo Institute for Economic Research at the University of Munich.
  2. Gilberto Tadeu Lima & Mark Setterfield, Jaylson Jair da Silveira, 2013. "Inflation Targeting and Macroeconomic Stability with Heterogeneous Inflation Expectations," Working Papers, Department of Economics 2013_11, University of São Paulo (FEA-USP).

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