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Differential Interpretation Of Information In Inflation Forecasts


  • Eugene Kandel
  • Ben-Zion Zilberfarb


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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  • Eugene Kandel & Ben-Zion Zilberfarb, 1999. "Differential Interpretation Of Information In Inflation Forecasts," The Review of Economics and Statistics, MIT Press, vol. 81(2), pages 217-226, May.
  • Handle: RePEc:tpr:restat:v:81:y:1999:i:2:p:217-226

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    References listed on IDEAS

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    8. Stephen G. Cecchetti, 1997. "Measuring short-run inflation for central bankers," Review, Federal Reserve Bank of St. Louis, issue May, pages 143-155.
    9. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
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    Cited by:

    1. Clements, Michael P., 2010. "Explanations of the inconsistencies in survey respondents' forecasts," European Economic Review, Elsevier, vol. 54(4), pages 536-549, May.
    2. Winkler, Bernhard, 2000. "Which kind of transparency? On the need for clarity in monetary policy-making," Working Paper Series 0026, European Central Bank.
    3. Hautsch, Nikolaus & Hess, Dieter & Veredas, David, 2011. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2733-2746, October.
    4. 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.
    5. Ottaviani, Marco & Sorensen, Peter Norman, 2006. "The strategy of professional forecasting," Journal of Financial Economics, Elsevier, vol. 81(2), pages 441-466, August.
    6. Massa, Massimo & Simonov, Andrei, 2005. "Is learning a dimension of risk?," Journal of Banking & Finance, Elsevier, vol. 29(10), pages 2605-2632, October.
    7. 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.
    8. Naohito Abe & Yuko Ueno, 2016. "The Mechanism of Inflation Expectation Formation among Consumers," UTokyo Price Project Working Paper Series 064, University of Tokyo, Graduate School of Economics.
    9. 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.
    10. Xuguang Sheng & Maya Thevenot, 2013. "Differential Interpretation of Public Information: Estimation and Inference," Working Papers 2013-03, American University, Department of Economics.
    11. Sheng, Xuguang (Simon) & Thevenot, Maya, 2015. "Quantifying differential interpretation of public information using financial analysts’ earnings forecasts," International Journal of Forecasting, Elsevier, vol. 31(2), pages 515-530.
    12. Jordi Pons-Novell, 2003. "Strategic bias, herding behaviour and economic forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(1), pages 67-77.
    13. Goldstein, Nathan & Zilberfarb, Ben-Zion, 2017. "Rationality and seasonality: Evidence from inflation forecasts," Economics Letters, Elsevier, vol. 150(C), pages 86-90.
    14. 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.
    15. 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.
    16. Abe, Naohito & Ueno, Yuko, 2016. "The Mechanism of Inflation Expectation Formation among Consumers," RCESR Discussion Paper Series DP16-1, Research Center for Economic and Social Risks, Institute of Economic Research, Hitotsubashi University.
    17. Marinovic, Iván & Ottaviani, Marco & Sorensen, Peter, 2013. "Forecasters’ Objectives and Strategies," Handbook of Economic Forecasting, Elsevier.
    18. 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.

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