IDEAS home Printed from
   My bibliography  Save this book chapter

Denoised least squars forecasting of GDP changes using indexes of consumer and business sentiment

In: Proceedings of the IFC Conference on "Initiatives to address data gaps revealed by the financial crisis", Basel, 25-26 August 2010


  • Antonis A Michis


Indexes of consumer and business sentiment are frequently characterized by measurement errors and short-term cyclical fluctuations that can distort their predictive accuracy for GDP changes. While measurement errors arise due to the survey sampling procedures that characterize these surveys, short-term cyclical fluctuations are generally linked with various exogenous and irregular factors that are not necessarily related to the economy. This paper shows, using data on the US economy, that applying wavelet denoising on indexes of consumer and business sentiment in the context of the linear regression model can overcome these limitations and can provide: (a) efficient coefficient estimates in models that explain consumer sentiment index variation; and (b) consistent coefficient estimates and predictions in models for GDP changes when using consumer and business sentiment indexes as predictors.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Antonis A Michis, 2011. "Denoised least squars forecasting of GDP changes using indexes of consumer and business sentiment," 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 383-392 Bank for International Settlements.
  • Handle: RePEc:bis:bisifc:34-26

    Download full text from publisher

    File URL:
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    1. Garner, C. Alan, 1981. "Economic determinants of consumer sentiment," Journal of Business Research, Elsevier, vol. 9(2), pages 205-220, June.
    2. Easaw, Joshy Z. & Garratt, Dean & Heravi, Saeed M., 2005. "Does consumer sentiment accurately forecast UK household consumption? Are there any comparisons to be made with the US?," Journal of Macroeconomics, Elsevier, vol. 27(3), pages 517-532, September.
    3. Yogo, Motohiro, 2008. "Measuring business cycles: A wavelet analysis of economic time series," Economics Letters, Elsevier, vol. 100(2), pages 208-212, August.
    4. Jason Bram & Sydney Ludvigson, 1998. "Does consumer confidence forecast household expenditure? a sentiment index horse race," Economic Policy Review, Federal Reserve Bank of New York, issue Jun, pages 59-78.
    5. Patrick M. Crowley, 2007. "A Guide To Wavelets For Economists ," Journal of Economic Surveys, Wiley Blackwell, vol. 21(2), pages 207-267, April.
    6. Garcia-Ferrer, Antonio & Bujosa-Brun, Marcos, 2000. "Forecasting OECD industrial turning points using unobserved components models with business survey data," International Journal of Forecasting, Elsevier, vol. 16(2), pages 207-227.
    7. Lemmens, A. & Croux, C. & Dekimpe, M.G., 2007. "Consumer confidence in Europe : United in diversity," Other publications TiSEM ea8c3268-2c0b-4fcc-9d4a-6, Tilburg University, School of Economics and Management.
    8. Fan, Chengze Simon & Wong, Phoebe, 1998. "Does consumer sentiment forecast household spending?: The Hong Kong case," Economics Letters, Elsevier, vol. 58(1), pages 77-84, January.
    9. Oller, Lars-Erik & Tallbom, Christer, 1996. "Smooth and timely business cycle indicators for noisy Swedish data," International Journal of Forecasting, Elsevier, vol. 12(3), pages 389-402, September.
    10. E. Philip Howrey, 2001. "The Predictive Power of the Index of Consumer Sentiment," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 32(1), pages 175-216.
    11. Miquel Clar & Juan-Carlos Duque & Rosina Moreno, 2007. "Forecasting business and consumer surveys indicators-a time-series models competition," Applied Economics, Taylor & Francis Journals, vol. 39(20), pages 2565-2580.
    Full references (including those not matched with items on IDEAS)

    More about this item

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access


    Access and download statistics


    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:bis:bisifc:34-26. 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: (Christian Beslmeisl). General contact details of provider: .

    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.