IDEAS home Printed from https://ideas.repec.org/p/lui/lleewp/15118.html
   My bibliography  Save this paper

The Predictive Content of Business Survey Indicators: evidence from SIGE

Author

Listed:
  • Tiziana Cesaroni

    ()

  • Stefano Iezzi

    ()

Abstract

Business surveys indicators represent an important tool in economic analysis and forecasting practices. While there is wide consensus on the coincident properties of such data, there is mixed evidence on their ability to predict macroeconomic developments in the short term. In this study we extend the previous research on business surveys predictive content by examining the leading properties of the main business survey indicators coming from the Italian Survey on Inflation and Growth Expectations (SIGE). To this end we provide a complete characterization of the business cycle properties of survey data (volatility, stationarity, turning points etc.) and we compare them with National Accounts reference series. We further analyze the forecast ability of the SIGE indicators to detect turning points using both discrete and continuous dynamic single equation models against their benchmark (B)ARIMA models. Overall the results indicate that SIGE business indicators are able to early detect turning points of their corresponding national account reference series. These findings are very important from a policy making point of view.

Suggested Citation

  • Tiziana Cesaroni & Stefano Iezzi, 2015. "The Predictive Content of Business Survey Indicators: evidence from SIGE," Working Papers LuissLab 15118, Dipartimento di Economia e Finanza, LUISS Guido Carli.
  • Handle: RePEc:lui:lleewp:15118
    as

    Download full text from publisher

    File URL: http://www.luiss.it/RePEc/pdf/lleewp/15118.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Cesaroni, Tatiana & Maccini, Louis & Malgarini, Marco, 2011. "Business cycle stylized facts and inventory behaviour: New evidence for the Euro area," International Journal of Production Economics, Elsevier, vol. 133(1), pages 12-24, September.
    2. Marcelle Chauvet & Simon Potter, 2005. "Forecasting recessions using the yield curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 77-103.
    3. Giancarlo Bruno & Claudio Lupi, 2004. "Forecasting industrial production and the early detection of turning points," Empirical Economics, Springer, vol. 29(3), pages 647-671, September.
    4. Michael Dueker, 2005. "Dynamic Forecasts of Qualitative Variables: A Qual VAR Model of U.S. Recessions," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 96-104, January.
    5. Tatiana Cesaroni, 2010. "Estimating potential output using business survey data in a svar framework," Economics Bulletin, AccessEcon, vol. 30(3), pages 2249-2258.
    6. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, July.
    7. Claveria, Oscar & Pons, Ernest & Ramos, Raul, 2007. "Business and consumer expectations and macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 47-69.
    8. Gerhard Bry & Charlotte Boschan, 1971. "Foreword to "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs"," NBER Chapters,in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages -1 National Bureau of Economic Research, Inc.
    9. 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.
    10. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
    11. Bergstrom, Reinhold, 1995. "The relationship between manufacturing production and different business survey series in Sweden 1968-;1992," International Journal of Forecasting, Elsevier, vol. 11(3), pages 379-393, September.
    12. Abberger, Klaus, 2007. "Qualitative business surveys and the assessment of employment -- A case study for Germany," International Journal of Forecasting, Elsevier, vol. 23(2), pages 249-258.
    13. Lemmens, Aurelie & Croux, Christophe & Dekimpe, Marnik G., 2005. "On the predictive content of production surveys: A pan-European study," International Journal of Forecasting, Elsevier, vol. 21(2), pages 363-375.
    14. Tatiana Cesaroni, 2011. "The cyclical behavior of the Italian business survey data," Empirical Economics, Springer, vol. 41(3), pages 747-768, December.
    15. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, July.
    16. Wheeler, Tracy, 2010. "What can we learn from surveys of business expectations?," Bank of England Quarterly Bulletin, Bank of England, vol. 50(3), pages 190-198.
    17. Lemmens, A. & Croux, C. & Dekimpe, M.G., 2005. "On the Predictive Content of Production Surveys : a Pan-European Study," Other publications TiSEM adab9f0e-7dfd-4dc4-bd92-b, Tilburg University, School of Economics and Management.
    18. Raffaele Tartaglia-Polcini, 2011. "Inflation forecasts from the Bank of Italy-Sole 24 Ore survey of expectations of inflation and growth," IFC Bulletins chapters,in: Bank for International Settlements (ed.), Proceedings of the IFC Conference on "Initiatives to address data gaps revealed by the financial crisis", Basel, 25-26 August 2010, volume 34, pages 278-292 Bank for International Settlements.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Business cycle; Business survey data; Turning points; cyclical analysis; Forecast accuracy; Macroeconomic forecasts;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:lui:lleewp:15118. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Giovanna Vallanti). General contact details of provider: http://edirc.repec.org/data/deluiit.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.