IDEAS home Printed from https://ideas.repec.org/p/bdi/opques/qef_258_15.html
   My bibliography  Save this paper

Easier said than done: the divergence between soft and hard data

Author

Listed:
  • Antonio M. Conti

    (Bank of Italy)

  • Concetta Rondinelli

    (Bank of Italy)

Abstract

Between the first half of 2013 and the summer of 2014, survey data pointed to a gradual recovery of economic activity, while the hard data continued to show persistent weakness. After providing statistical evidence to support the hypothesis that, during the sovereign debt crisis, the relationship between soft and hard variables for the Italian economy has weakened, the paper evaluates some possible explanations for this gap. The micro data for the quarterly survey conducted by the Bank of Italy � Il Sole 24 Ore on growth and inflation expectations tend to rule out the hypothesis that the gap between the qualitative and quantitative indicators comes from selection effects due to the progressive exclusion from the sample of economically distressed firms. Furthermore, the prolonged recession seems to have modified firms� expectations, leading to a downward revision of production plans and the setting of a �new normal� situation. Therefore, firms may still have expressed favorable expectations for the economic outlook in spite of cyclically slack activity.

Suggested Citation

  • Antonio M. Conti & Concetta Rondinelli, 2015. "Easier said than done: the divergence between soft and hard data," Questioni di Economia e Finanza (Occasional Papers) 258, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_258_15
    as

    Download full text from publisher

    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2015-0258/QEF_258.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Valentina Aprigliano, 2011. "The relationship between the PMI and the Italian index of industrial production and the impact of the latest economic crisis," Temi di discussione (Economic working papers) 820, Bank of Italy, Economic Research and International Relations Area.
    2. 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.
    3. Barsky, Robert B. & Sims, Eric R., 2011. "News shocks and business cycles," Journal of Monetary Economics, Elsevier, vol. 58(3), pages 273-289.
    4. Paul Beaudry & Franck Portier, 2006. "Stock Prices, News, and Economic Fluctuations," American Economic Review, American Economic Association, vol. 96(4), pages 1293-1307, September.
    5. Mario Forni & Luca Gambetti & Marco Lippi & Luca Sala, 2017. "Noisy News in Business Cycles," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(4), pages 122-152, October.
    6. 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.
    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. Maria Rita Ippoliti & Fabiana Sartor & Luigi Martone, 2021. "Trade surveys: qualitative and quantitative indicators," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 75(4), pages 75-85, October-D.
    2. G. Bruno & L. Crosilla & P. Margani, 2019. "Inspecting the Relationship Between Business Confidence and Industrial Production: Evidence on Italian Survey Data," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 15(1), pages 1-24, April.

    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. 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.
    2. Miyamoto, Wataru & Nguyen, Thuy Lan, 2020. "The expectational effects of news in business cycles: Evidence from forecast data," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 184-200.
    3. Ryan Chahrour & Kyle Jurado, 2018. "News or Noise? The Missing Link," American Economic Review, American Economic Association, vol. 108(7), pages 1702-1736, July.
    4. Christoph Görtz & John D. Tsoukalas & Francesco Zanetti, 2022. "News Shocks under Financial Frictions," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(4), pages 210-243, October.
    5. 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.
    6. Andrea Gazzani, 2020. "News and noise bubbles in the housing market," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 36, pages 46-72, April.
    7. Rabah Arezki & Valerie A. Ramey & Liugang Sheng, 2017. "News Shocks in Open Economies: Evidence from Giant Oil Discoveries," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(1), pages 103-155.
    8. Luca Gambetti & Nicolò Maffei-Faccioli & Sarah Zoi, 2022. "Bad News, Good News: Coverage and Response Asymmetries," Working Paper 2022/8, Norges Bank.
    9. 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.
    10. Daniela Fantozzi & Alessio Muscarnera, 2021. "A News-based Policy Index for Italy: Expectations and Fiscal Policy," CEIS Research Paper 509, Tor Vergata University, CEIS, revised 11 Mar 2021.
    11. Stéphane Dees, 2017. "The role of confidence shocks in business cycles and their global dimension," International Economics, CEPII research center, issue 151, pages 48-65.
    12. 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.
    13. Clements, Michael P. & Galvão, Ana Beatriz, 2021. "Measuring the effects of expectations shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 124(C).
    14. Forni, Mario & Gambetti, Luca, 2014. "Sufficient information in structural VARs," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 124-136.
    15. Patrick Fève & Alain Guay, 2019. "Sentiments in SVARs," The Economic Journal, Royal Economic Society, vol. 129(618), pages 877-896.
    16. Larsen, Vegard H. & Thorsrud, Leif A., 2019. "The value of news for economic developments," Journal of Econometrics, Elsevier, vol. 210(1), pages 203-218.
    17. Mario Forni & Luca Gambetti & Luca Sala, 2018. "Fundamentalness, Granger Causality and Aggregation," Center for Economic Research (RECent) 139, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    18. 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).
    19. Metiu, Norbert, 2021. "Anticipation effects of protectionist U.S. trade policies," Journal of International Economics, Elsevier, vol. 133(C).
    20. Di Bella, Gabriel & Grigoli, Francesco, 2019. "Optimism, pessimism, and short-term fluctuations," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 79-96.

    More about this item

    Keywords

    survey data; confidence; industrial production; new normal;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

    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:bdi:opques:qef_258_15. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bdigvit.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.