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Expectation Formation Following Large, Unexpected Shocks

Author

Listed:
  • Scott R. Baker

    (Northwestern University)

  • Tucker S. McElroy

    (U.S. Census Bureau)

  • Xuguang S. Sheng

    (American University)

Abstract

By matching a large database of individual macroforecaster data with the universe of sizable natural disasters across 54 countries, we identify a set of new stylized facts: forecasters are persistently heterogeneous in how often they issue or revise a forecast; information rigidity declines significantly following large, unexpected natural disaster shocks; and disagreement decreases among inattentive agents while it might increase for attentive ones. We develop a learning model that captures the two channels through which natural disaster shocks affect expectation formation: attention effect—the visibly large shocks induce immediate and synchronized updating of information for inattentive agents—and uncertainty effect—attentive agents might increase their acquisition of private information to compensate for the higher uncertainty after shocks.

Suggested Citation

  • Scott R. Baker & Tucker S. McElroy & Xuguang S. Sheng, 2020. "Expectation Formation Following Large, Unexpected Shocks," The Review of Economics and Statistics, MIT Press, vol. 102(2), pages 287-303, May.
  • Handle: RePEc:tpr:restat:v:102:y:2020:i:2:p:287-303
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    References listed on IDEAS

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    1. 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, President and Fellows of Harvard College, vol. 117(4), pages 1295-1328.
    2. Yuriy Gorodnichenko, 2008. "Endogenous information, menu costs and inflation persistence," NBER Working Papers 14184, National Bureau of Economic Research, Inc.
    3. Alberto Cavallo & Eduardo Cavallo & Roberto Rigobon, 2014. "Prices and Supply Disruptions during Natural Disasters," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(S2), pages 449-471, November.
    4. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    5. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    6. Neusser, Klaus, 2016. "A topological view on the identification of structural vector autoregressions," Economics Letters, Elsevier, vol. 144(C), pages 107-111.
    7. Harald Uhlig, 1997. "Bayesian Vector Autoregressions with Stochastic Volatility," Econometrica, Econometric Society, vol. 65(1), pages 59-74, January.
    8. Charles F. Manski, 2018. "Survey Measurement of Probabilistic Macroeconomic Expectations: Progress and Promise," NBER Macroeconomics Annual, University of Chicago Press, vol. 32(1), pages 411-471.
    9. Olivier Coibion, 2010. "Testing the Sticky Information Phillips Curve," The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 87-101, February.
    10. Christopher D. Carroll, 2003. "Macroeconomic Expectations of Households and Professional Forecasters," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(1), pages 269-298.
    11. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    12. Ricardo Reis, 2006. "Inattentive Producers," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(3), pages 793-821.
    13. Branch, William A., 2007. "Sticky information and model uncertainty in survey data on inflation expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 31(1), pages 245-276, January.
    14. repec:bla:revinw:v:60:y:2014:i::p:s449-s471 is not listed on IDEAS
    15. Eichenbaum, Martin & Parker, Jonathan A. (ed.), . "NBER Macroeconomics Annual 2017," National Bureau of Economic Research Books, University of Chicago Press, number 9780226577838, July.
    16. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    17. Veldkamp, Laura & Orlik, Anna, 2014. "Understanding Uncertainty Shocks and the Role of Black Swans," CEPR Discussion Papers 10147, C.E.P.R. Discussion Papers.
    18. Eichenbaum, Martin & Parker, Jonathan A. (ed.), 2018. "NBER Macroeconomics Annual 2017," National Bureau of Economic Research Books, University of Chicago Press, number 9780226577661, November.
    19. 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.
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