IDEAS home Printed from https://ideas.repec.org/a/tpr/restat/v102y2020i2p287-303.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/rest_a_00826
    Download Restriction: Access to PDF is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Ricardo Reis, 2006. "Inattentive Producers," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(3), pages 793-821.
    4. 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.
    5. Olivier Coibion, 2010. "Testing the Sticky Information Phillips Curve," The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 87-101, February.
    6. 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.
    7. 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.
    8. Yuriy Gorodnichenko, 2008. "Endogenous information, menu costs and inflation persistence," NBER Working Papers 14184, National Bureau of Economic Research, Inc.
    9. Anna Orlik & Laura Veldkamp, 2014. "Understanding Uncertainty Shocks and the Role of Black Swans," NBER Working Papers 20445, National Bureau of Economic Research, Inc.
    10. Eichenbaum, Martin & Parker, Jonathan A. (ed.), . "NBER Macroeconomics Annual 2017," National Bureau of Economic Research Books, University of Chicago Press, number 9780226577838, July.
    11. 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.
    12. Eichenbaum, Martin & Parker, Jonathan A. (ed.), 2018. "NBER Macroeconomics Annual 2017," National Bureau of Economic Research Books, University of Chicago Press, number 9780226577661, December.
    13. 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.
    14. Neusser, Klaus, 2016. "A topological view on the identification of structural vector autoregressions," Economics Letters, Elsevier, vol. 144(C), pages 107-111.
    15. 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.
    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. Harald Uhlig, 1997. "Bayesian Vector Autoregressions with Stochastic Volatility," Econometrica, Econometric Society, vol. 65(1), pages 59-74, January.
    18. 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.
    19. repec:bla:revinw:v:60:y:2014:i::p:s449-s471 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aaronson, Daniel & Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael & Sacks, Daniel W. & Seo, Boyoung, 2022. "Forecasting unemployment insurance claims in realtime with Google Trends," International Journal of Forecasting, Elsevier, vol. 38(2), pages 567-581.
    2. Constantin Bürgi & Julio L. Ortiz, 2022. "Overreaction through Anchoring," CESifo Working Paper Series 10193, CESifo.
    3. Glas, Alexander & Heinisch, Katja, 2021. "Conditional macroeconomic forecasts: Disagreement, revisions and forecast errors," IWH Discussion Papers 7/2021, Halle Institute for Economic Research (IWH).
    4. Andrew C. Chang & Trace J. Levinson, 2023. "Raiders of the lost high‐frequency forecasts: New data and evidence on the efficiency of the Fed's forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 88-104, January.
    5. An, Zidong & Liu, Dingqian & Wu, Yuzheng, 2021. "Expectation formation following pandemic events," Economics Letters, Elsevier, vol. 200(C).
    6. de Mendonça, Helder Ferreira & Vereda, Luciano & Araujo, Mateus de Azevedo, 2022. "What type of information calls the attention of forecasters? Evidence from survey data in an emerging market," Journal of International Money and Finance, Elsevier, vol. 129(C).
    7. Brent Meyer & Nicholas B. Parker & Xuguang Sheng, 2021. "Unit Cost Expectations and Uncertainty: Firms' Perspectives on Inflation," FRB Atlanta Working Paper 2021-12a, Federal Reserve Bank of Atlanta.
    8. Morikawa, Masayuki, 2022. "Uncertainty in long-term macroeconomic forecasts: Ex post evaluation of forecasts by economics researchers," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 8-15.
    9. Christopher S Sutherland, 2022. "Forward guidance and expectation formation: A narrative approach," BIS Working Papers 1024, Bank for International Settlements.
    10. Constantin Bürgi & Tara M. Sinclair, 2021. "What does forecaster disagreement tell us about the state of the economy?," Applied Economics Letters, Taylor & Francis Journals, vol. 28(1), pages 49-53, January.
    11. Bannigidadmath, Deepa & Narayan, Paresh Kumar, 2021. "Economic news and the cross-section of commodity futures returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).
    12. Anat Bracha & Jenny Tang, 2022. "Inflation Levels and (In)Attention," Working Papers 22-4, Federal Reserve Bank of Boston.
    13. Sarantis Tsiaplias, 2021. "Consumer inflation expectations, income changes and economic downturns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 784-807, September.
    14. Dietrich, Alexander M. & Kuester, Keith & Müller, Gernot J. & Schoenle, Raphael, 2022. "News and uncertainty about COVID-19: Survey evidence and short-run economic impact," Journal of Monetary Economics, Elsevier, vol. 129(S), pages 35-51.
    15. An, Zidong & Binder, Carola & Sheng, Xuguang Simon, 2023. "Gas price expectations of Chinese households," Energy Economics, Elsevier, vol. 120(C).
    16. Meinerding, Christoph & Poinelli, Andrea & Schüler, Yves, 2022. "Inflation expectations and climate concern," Discussion Papers 12/2022, Deutsche Bundesbank.
    17. Imane El Ouadghiri & Remzi Uctum, 2020. "Macroeconomic expectations and time varying heterogeneity:evidence from individual survey data," Applied Economics, Taylor & Francis Journals, vol. 52(23), pages 2443-2459, May.
    18. Xu, Xin & Xu, Xiaoguang, 2023. "Monetary policy transmission modeling and policy responses," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    19. Lena Dräger & Klaus Gründler & Niklas Potrafke, 2022. "Political Shocks and Inflation Expectations: Evidence from the 2022 Russian Invasion of Ukraine," ifo Working Paper Series 371, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    20. Kuang, Pei & Luca, Davide & Wei, Zhiwu & Yao, Yao, 2023. "Great or grim? Disagreement about Brexit, economic expectations and household spending," LSE Research Online Documents on Economics 119200, London School of Economics and Political Science, LSE Library.
    21. Christopher S. Sutherland, 2020. "Forward Guidance and Expectation Formation: A Narrative Approach," Staff Working Papers 20-40, Bank of Canada.
    22. Carola Binder & Tucker S. Mcelroy & Xuguang S. Sheng, 2022. "The Term Structure of Uncertainty: New Evidence from Survey Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(1), pages 39-71, February.
    23. Fabrizio Ferriani & Andrea Gazzani & Filippo Natoli, 2023. "Flight to climatic safety: local natural disasters and global portfolio flows," Temi di discussione (Economic working papers) 1420, Bank of Italy, Economic Research and International Relations Area.
    24. Zidong An & Salem Abo‐Zaid & Xuguang Simon Sheng, 2023. "Inattention and the impact of monetary policy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 623-643, June.
    25. Vereda, Luciano & Savignon, João & Gouveia da Silva, Tarciso, 2021. "A new method to assess the degree of information rigidity using fixed-event forecasts," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1576-1589.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," American Economic Review, American Economic Association, vol. 105(8), pages 2644-2678, August.
    2. 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.
    3. James M. Nason & Gregor W. Smith, 2021. "Measuring the slowly evolving trend in US inflation with professional forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 1-17, January.
    4. Carrera Cesar, 2012. "Estimating Information Rigidity Using Firms' Survey Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 12(1), pages 1-34, June.
    5. James M. Nason & Gregor W. Smith, 2013. "Reverse Kalman filtering U.S. inflation with sticky professional forecasts," Working Papers 13-34, Federal Reserve Bank of Philadelphia.
    6. Andrade, Philippe & Crump, Richard K. & Eusepi, Stefano & Moench, Emanuel, 2016. "Fundamental disagreement," Journal of Monetary Economics, Elsevier, vol. 83(C), pages 106-128.
    7. Andrade, Philippe & Le Bihan, Hervé, 2013. "Inattentive professional forecasters," Journal of Monetary Economics, Elsevier, vol. 60(8), pages 967-982.
    8. Conrad, Christian & Lahiri, Kajal, 2023. "Heterogeneous expectations among professional forecasters," ZEW Discussion Papers 23-062, ZEW - Leibniz Centre for European Economic Research.
    9. Paul Hubert, 2014. "FOMC Forecasts as a Focal Point for Private Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(7), pages 1381-1420, October.
    10. Oleksiy Kryvtsov, 2005. "Information Flows and Aggregate Persistence," Computing in Economics and Finance 2005 416, Society for Computational Economics.
    11. Larsen, Vegard H. & Thorsrud, Leif Anders & Zhulanova, Julia, 2021. "News-driven inflation expectations and information rigidities," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 507-520.
    12. Kim, Insu & Kim, Young Se, 2019. "Inattentive agents and inflation forecast error dynamics: A Bayesian DSGE approach," Journal of Macroeconomics, Elsevier, vol. 62(C).
    13. Berge, Travis J., 2018. "Understanding survey-based inflation expectations," International Journal of Forecasting, Elsevier, vol. 34(4), pages 788-801.
    14. Frédérique Bec & Raouf Boucekkine & Caroline Jardet, 2023. "Why Are Inflation Forecasts Sticky? Theory and Application to France and Germany," International Journal of Central Banking, International Journal of Central Banking, vol. 19(4), pages 215-249, October.
    15. de Mendonça, Helder Ferreira & Vereda, Luciano & Araujo, Mateus de Azevedo, 2022. "What type of information calls the attention of forecasters? Evidence from survey data in an emerging market," Journal of International Money and Finance, Elsevier, vol. 129(C).
    16. Carrera, César & Ramírez-Rondán, N.R., 2019. "Inflation, Information Rigidity, And The Sticky Information Phillips Curve," Macroeconomic Dynamics, Cambridge University Press, vol. 23(7), pages 2597-2615, October.
    17. Easaw Joshy & Golinelli Roberto, 2010. "Households Forming Inflation Expectations: Active and Passive Absorption Rates," The B.E. Journal of Macroeconomics, De Gruyter, vol. 10(1), pages 1-32, November.
    18. Paul Hubert & Harun Mirza, 2019. "The role of forward‐ and backward‐looking information for inflation expectations formation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(8), pages 733-748, December.
    19. repec:hal:spmain:info:hdl:2441/3v5mev848s8148gjqcbf4mva5q is not listed on IDEAS
    20. Dovern, Jonas & Fritsche, Ulrich & Loungani, Prakash & Tamirisa, Natalia, 2015. "Information rigidities: Comparing average and individual forecasts for a large international panel," International Journal of Forecasting, Elsevier, vol. 31(1), pages 144-154.
    21. Lanne, Markku & Luoma, Arto & Luoto, Jani, 2009. "A naïve sticky information model of households' inflation expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 33(6), pages 1332-1344, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tpr:restat:v:102:y:2020:i:2:p:287-303. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kelly McDougall (email available below). General contact details of provider: https://direct.mit.edu/journals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.