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Differential Interpretation Of Information In Inflation Forecasts

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  • Eugene Kandel
  • Ben-Zion Zilberfarb

Abstract

We test the hypothesis associated with a standard assumption in the theoretical literature on learning: that economic agents interpret information identically. We use a data set based on a survey of Israeli business executives forecasting future inflation. One of the main advantages of using this data is that a major change in the inflation regime in 1985 can be treated as a natural experiment in new beliefs formation. We develop a methodology for testing this hypothesis and find evidence that is inconsistent with the identical-interpretation hypothesis, but is consistent with the proposed alternative. © 1999 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

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Bibliographic Info

Article provided by MIT Press in its journal The Review of Economics and Statistics.

Volume (Year): 81 (1999)
Issue (Month): 2 (May)
Pages: 217-226

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Handle: RePEc:tpr:restat:v:81:y:1999:i:2:p:217-226

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Cited by:
  1. Clements, Michael P., 2014. "Probability distributions or point predictions? Survey forecasts of US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 99-117.
  2. Nikolaus Hautsch & Dieter Hess & David Veredas, 2010. "The Impact of Macroeconomic News on Quote Adjustments, Noise, and Informational Volatility," SFB 649 Discussion Papers SFB649DP2010-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  3. Granato, Jim & Guse, Eran A. & Wong, M. C. Sunny, 2008. "Learning From The Expectations Of Others," Macroeconomic Dynamics, Cambridge University Press, vol. 12(03), pages 345-377, June.
  4. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
  5. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
  6. Ottaviani, Marco & Sorensen, Peter Norman, 2006. "The strategy of professional forecasting," Journal of Financial Economics, Elsevier, vol. 81(2), pages 441-466, August.
  7. Hammad A. Siddiqi, 2006. "Is it Social Influence on Beliefs Under Ambiguity? A Possible Explanation for Volatility Clustering," Microeconomics Working Papers 22279, East Asian Bureau of Economic Research.
  8. Siddiqi, Hammad, 2006. "Belief merging and revision under social influence: An explanation for the volatility clustering puzzle," MPRA Paper 657, University Library of Munich, Germany.
  9. Xuguang Sheng & Maya Thevenot, 2013. "Differential Interpretation of Public Information: Estimation and Inference," Working Papers 2013-03, American University, Department of Economics.
  10. Massa, Massimo & Simonov, Andrei, 2005. "Is learning a dimension of risk?," Journal of Banking & Finance, Elsevier, vol. 29(10), pages 2605-2632, October.
  11. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.
  12. Winkler, Bernhard, 2000. "Which kind of transparency? On the need for clarity in monetary policy-making," Working Paper Series 0026, European Central Bank.
  13. Jordi Pons-Novell, 2003. "Strategic bias, herding behaviour and economic forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(1), pages 67-77.

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