IDEAS home Printed from https://ideas.repec.org/p/wrk/wrkemf/31.html
   My bibliography  Save this paper

Measuring the Effects of Expectations Shocks

Author

Listed:
  • Clements, Michael P.

    (University of Reading)

  • Galvao, Ana Beatriz

    (University of Warwick)

Abstract

We show that expectation shocks -- revisions in expectations unrelated to changes in current economic fundamentals -- have positive significant effects on US economic activity. To measure the expectation shocks, we estimate a mixed-frequency VAR model that allows economic conditions in the current quarter to affect current-quarter GDP expectations. The expectations shock is estimated with real-time data so such shocks do not suffer a “look-forward” bias by incorporating future data revisions. Dynamic responses are estimated with the aid of a quarterly VAR and using older vintages as instruments to account for measurement errors in the observed values. Expectations shocks explain 10% of the two-year variation of output, investment, consumption and hours. We find that expectations shocks are correlated with alternative belief-based shocks, but nevertheless have significant additional short-run effects.

Suggested Citation

  • Clements, Michael P. & Galvao, Ana Beatriz, 2019. "Measuring the Effects of Expectations Shocks," EMF Research Papers 31, Economic Modelling and Forecasting Group.
  • Handle: RePEc:wrk:wrkemf:31
    as

    Download full text from publisher

    File URL: https://warwick.ac.uk/fac/soc/wbs/subjects/finance/mpf/working-papers/emf_wp_31.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Christina D. Romer & David H. Romer, 2004. "A New Measure of Monetary Shocks: Derivation and Implications," American Economic Review, American Economic Association, vol. 94(4), pages 1055-1084, September.
    2. Danilo Cascaldi-Garcia, 2017. "News Shocks and the Slope of the Term Structure of Interest Rates: Comment," American Economic Review, American Economic Association, vol. 107(10), pages 3243-3249, October.
    3. S. Borağan Aruoba, 2008. "Data Revisions Are Not Well Behaved," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2‐3), pages 319-340, March.
    4. Pedro Bordalo & Nicola Gennaioli & Yueran Ma & Andrei Shleifer, 2020. "Overreaction in Macroeconomic Expectations," American Economic Review, American Economic Association, vol. 110(9), pages 2748-2782, September.
    5. Patrick Fève & Alain Guay, 2019. "Sentiments in SVARs," The Economic Journal, Royal Economic Society, vol. 129(618), pages 877-896.
    6. Paul Beaudry & Franck Portier, 2014. "News-Driven Business Cycles: Insights and Challenges," Journal of Economic Literature, American Economic Association, vol. 52(4), pages 993-1074, December.
    7. André Kurmann & Eric Sims, 2021. "Revisions in Utilization-Adjusted TFP and Robust Identification of News Shocks," The Review of Economics and Statistics, MIT Press, vol. 103(2), pages 216-235, May.
    8. Alastair Cunningham & Jana Eklund & Chris Jeffery & George Kapetanios & Vincent Labhard, 2009. "A State Space Approach to Extracting the Signal From Uncertain Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 173-180, March.
    9. 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.
    10. Barsky, Robert B. & Sims, Eric R., 2011. "News shocks and business cycles," Journal of Monetary Economics, Elsevier, vol. 58(3), pages 273-289.
    11. Paul Beaudry & Franck Portier, 2006. "Stock Prices, News, and Economic Fluctuations," American Economic Review, American Economic Association, vol. 96(4), pages 1293-1307, September.
    12. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    13. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    14. Croushore, Dean & Evans, Charles L., 2006. "Data revisions and the identification of monetary policy shocks," Journal of Monetary Economics, Elsevier, vol. 53(6), pages 1135-1160, September.
    15. Milani, Fabio, 2017. "Sentiment and the U.S. business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 289-311.
    16. Mario Forni & Luca Gambetti & Luca Sala, 2019. "Structural VARs and noninvertible macroeconomic models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 221-246, March.
    17. James H. Stock & Mark W. Watson, 2018. "Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments," Economic Journal, Royal Economic Society, vol. 128(610), pages 917-948, May.
    18. Anthony Garratt & Kevin Lee & Emi Mise & Kalvinder Shields, 2008. "Real-Time Representations of the Output Gap," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 792-804, November.
    19. Thuy Lan Nguyen & Wataru Miyamoto, 2014. "News shocks and Business cycles: Evidence from forecast data," 2014 Meeting Papers 259, Society for Economic Dynamics.
    20. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    21. repec:taf:jnlbes:v:30:y:2012:i:2:p:181-190 is not listed on IDEAS
    22. Robert B. Barsky & Eric R. Sims, 2012. "Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence," American Economic Review, American Economic Association, vol. 102(4), pages 1343-1377, June.
    23. Joseph Engelberg & Charles F. Manski & Jared Williams, 2011. "Assessing the temporal variation of macroeconomic forecasts by a panel of changing composition," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(7), pages 1059-1078, November.
    24. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    25. Sylvain Leduc & Keith Sill, 2013. "Expectations and Economic Fluctuations: An Analysis Using Survey Data," The Review of Economics and Statistics, MIT Press, vol. 95(4), pages 1352-1367, October.
    26. Alastair Cunningham & Jana Eklund & Chris Jeffery & George Kapetanios & Vincent Labhard, 2009. "A State Space Approach to Extracting the Signal From Uncertain Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 173-180, March.
    27. Fabio Milani, 2011. "Expectation Shocks and Learning as Drivers of the Business Cycle," Economic Journal, Royal Economic Society, vol. 121(552), pages 379-401, May.
    28. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    29. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    30. Alessandro Girardi & Andreas Reuter, 2017. "New uncertainty measures for the euro area using survey data," Oxford Economic Papers, Oxford University Press, vol. 69(1), pages 278-300.
    31. N. Gregory Mankiw & Matthew D. Shapiro, 1986. "News or Noise? An Analysis of GNP Revisions," NBER Working Papers 1939, National Bureau of Economic Research, Inc.
    32. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
    33. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    34. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
    35. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575.
    36. J. B. Taylor & Harald Uhlig (ed.), 2016. "Handbook of Macroeconomics," Handbook of Macroeconomics, Elsevier, edition 1, volume 2, number 2.
    37. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    38. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    39. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    40. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    41. Michael P. Clements & Ana Beatriz Galvão, 2017. "Predicting Early Data Revisions to U.S. GDP and the Effects of Releases on Equity Markets," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 389-406, July.
    42. Zarnowitz, Victor & Lambros, Louis A, 1987. "Consensus and Uncertainty in Economic Prediction," Journal of Political Economy, University of Chicago Press, vol. 95(3), pages 591-621, June.
    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. Ahmed, M. Iqbal & Cassou, Steven P., 2021. "Asymmetries in the effects of unemployment expectation shocks as monetary policy shifts with economic conditions," Economic Modelling, Elsevier, vol. 100(C).
    2. Jonathan J Adams & Philip Barrett, 2022. "Shocks to Inflation Expectations," Working Papers 001007, University of Florida, Department of Economics.
    3. Christoph Görtz & Christopher Gunn & Thomas Lubik, "undated". "What Drives Inventory Accumulation? News on Rates of Return and Marginal Costs," Carleton Economic Papers 19-09, Carleton University, Department of Economics.
    4. Klein, Tony, 2021. "Agree to Disagree? Predictions of U.S. Nonfarm Payroll Changes between 2008 and 2020 and the Impact of the COVID19 Labor Shock," QBS Working Paper Series 2021/07, Queen's University Belfast, Queen's Business School.
    5. Danilo Cascaldi-Garcia, 2022. "Forecast Revisions as Instruments for News Shocks," International Finance Discussion Papers 1341, Board of Governors of the Federal Reserve System (U.S.).
    6. Lin, Jilei & Eck, Daniel J., 2021. "Minimizing post-shock forecasting error through aggregation of outside information," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1710-1727.
    7. Clements, Michael P., 2021. "Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 37(2), pages 634-646.
    8. Ma, Xiaohan & Samaniego, Roberto, 2022. "Business cycle dynamics when neutral and investment-specific technology shocks are imperfectly observable," Journal of Mathematical Economics, Elsevier, vol. 101(C).
    9. Klein, Tony, 2022. "Agree to disagree? Predictions of U.S. nonfarm payroll changes between 2008 and 2020 and the impact of the COVID19 labor shock," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 264-286.

    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. Danilo Cascaldi-Garcia, 2022. "Forecast Revisions as Instruments for News Shocks," International Finance Discussion Papers 1341, Board of Governors of the Federal Reserve System (U.S.).
    2. Nadav Ben Zeev, 2019. "Is There A Single Shock That Drives The Majority Of Business Cycle Fluctuations?," Working Papers 1906, Ben-Gurion University of the Negev, Department of Economics.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Zeno Enders & Michael Kleemann & Gernot J. Muller, 2021. "Growth Expectations, Undue Optimism, and Short-Run Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 103(5), pages 905-921, December.
    5. Anthony Garratt & Kevin Lee & Kalvinder Shields, 2018. "The role of uncertainty, sentiment and cross‐country interactions in G7 output dynamics," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(2), pages 391-418, May.
    6. Vegard H�ghaug Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Papers No 6/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    7. Angeletos, G.-M. & Lian, C., 2016. "Incomplete Information in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1065-1240, Elsevier.
    8. Michael P. Clements, 2017. "Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 420-433, July.
    9. Danilo Cascaldi-Garcia, 2017. "Amplification effects of news shocks through uncertainty," 2017 Papers pca1251, Job Market Papers.
    10. Clements, Michael P., 2019. "Do forecasters target first or later releases of national accounts data?," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1240-1249.
    11. Metiu, Norbert, 2021. "Anticipation effects of protectionist U.S. trade policies," Journal of International Economics, Elsevier, vol. 133(C).
    12. Clements, Michael P. & Beatriz Galvao, Ana, 2010. "Real-time Forecasting of Inflation and Output Growth in the Presence of Data Revisions," Economic Research Papers 270771, University of Warwick - Department of Economics.
    13. Danilo Cascaldi‐Garcia & Ana Beatriz Galvao, 2021. "News and Uncertainty Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(4), pages 779-811, June.
    14. Laurent Ferrara & Pierre Guérin, 2018. "What are the macroeconomic effects of high‐frequency uncertainty shocks?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 662-679, August.
    15. George‐Marios Angeletos & Fabrice Collard & Harris Dellas, 2018. "Quantifying Confidence," Econometrica, Econometric Society, vol. 86(5), pages 1689-1726, September.
    16. Michael Ryan, 2020. "An Anchor in Stormy Seas: Does Reforming Economic Institutions Reduce Uncertainty? Evidence from New Zealand," Working Papers in Economics 20/11, University of Waikato.
    17. Laura Nowzohour & Livio Stracca, 2020. "More Than A Feeling: Confidence, Uncertainty, And Macroeconomic Fluctuations," Journal of Economic Surveys, Wiley Blackwell, vol. 34(4), pages 691-726, September.
    18. Osnat Zohar, 2021. "Cyclicality of Uncertainty and Disagreement," Bank of Israel Working Papers 2021.09, Bank of Israel.
    19. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    20. Kyle Jurado, 2016. "Advance Information and Distorted Beliefs in Macroeconomic and Financial Fluctuations," 2016 Meeting Papers 154, Society for Economic Dynamics.

    More about this item

    Keywords

    mixed-frequency Vector Autoregressive Models ; real-time data ; measurement errors ; expectational shocks;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:wrk:wrkemf:31. 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: Ana Galvão (email available below). General contact details of provider: https://edirc.repec.org/data/emwaruk.html .

    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.