IDEAS home Printed from https://ideas.repec.org/p/kof/wpskof/10-249.html
   My bibliography  Save this paper

Assessing Predictive Content of the KOF Barometer in Real Time

Author

Listed:
  • Boriss Siliverstovs

Abstract

We investigate whether the KOF Barometer-a leading indicator regularly released by the KOF Swiss Economic Institute-can be useful for short-term out-of-sample prediction of year-on-year quarterly real GDP growth rates in Switzerland. We find that the KOF Barometer appears to be useful for prediction of GDP growth rates. Even the earliest forecasts, made seven months ahead of the first official GDP estimate, allow us to predict GDP growth rates more accurately than forecasts based on an univariate autoregressive model. At every subsequent forecast round as new monthly releases of the KOF Barometer become available we observe a steady increase in forecast accuracy.

Suggested Citation

  • Boriss Siliverstovs, 2010. "Assessing Predictive Content of the KOF Barometer in Real Time," KOF Working papers 10-249, KOF Swiss Economic Institute, ETH Zurich.
    • Handle: RePEc:kof:wpskof:10-249
    DOI: 10.3929/ethz-a-005975789
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.3929/ethz-a-005975789
    Download Restriction: no
    File URL: https://libkey.io/10.3929/ethz-a-005975789?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hansson, Jesper & Jansson, Per & Lof, Marten, 2005. "Business survey data: Do they help in forecasting GDP growth?," International Journal of Forecasting, Elsevier, vol. 21(2), pages 377-389.
    2. Balke, Nathan S & Petersen, D'Ann, 2002. "How Well Does the Beige Book Reflect Economic Activity? Evaluating Qualitative Information Quantitatively," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(1), pages 114-136, February.
    3. Jan Jacobs & Jan-Egbert Sturm, 2009. "The information content of KOF indicators on Swiss current account data revisions," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2008(2), pages 161-181.
    4. Hanssens, Dominique M & Vanden Abeele, Pierre M, 1987. "A Time-Series Study of the Formation and Predictive Performance of EEC Production Survey Expectations," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 507-519, October.
    5. 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.
    6. Lein, Sarah M., 2010. "When do firms adjust prices? Evidence from micro panel data," Journal of Monetary Economics, Elsevier, vol. 57(6), pages 696-715, September.
    7. Kauppi, Eija & Lassila, Jukka & Teräsvirta, Timo, 1996. "Short-Term Forecasting of Industrial Production with Business Survey Data: Experience from Finland's Great Depression," Discussion Papers 546, The Research Institute of the Finnish Economy.
    8. Michael Graff, 2006. "Ein multisektoraler Sammelindikator für die Schweizer Konjunktur," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 142(IV), pages 529-577, December.
    9. Richard Etter & Michael Graff, 2003. "Estimating and Forecasting Production and Orders in Manufacturing Industry from Business Survey Data: Evidence from Switzerland, 1990-2003," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 139(IV), pages 507-533, December.
    10. Richard Etter & Michael Graff, 2004. "Coincident and Leading Indicators of Manufacturing Industry," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(1), pages 109-131.
    11. Victor Zarnowitz, 1973. "A Review of Cyclical Indicators for the United States: Preliminary Results," NBER Working Papers 0006, National Bureau of Economic Research, Inc.
    12. 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.
    13. Golinelli, Roberto & Parigi, Giuseppe, 2008. "Real-time squared: A real-time data set for real-time GDP forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 368-385.
    14. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    15. Oller, Lars-Erik, 1990. "Forecasting the business cycle using survey data," International Journal of Forecasting, Elsevier, vol. 6(4), pages 453-461, December.
    16. Christian Mueller & Aniela Wirz & Nora Sydow, 2007. "A Note on the Carlson-Parkin Method of Quantifying Qualitative Data," KOF Working papers 07-168, KOF Swiss Economic Institute, ETH Zurich.
    17. 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.
    18. Klaus Abberger, 2007. "Forecasting Quarter-on-Quarter Changes of German GDP with Monthly Business Tendency Survey Results," ifo Working Paper Series 40, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    19. James H. Stock & Mark W. Watson, 1988. "A Probability Model of The Coincident Economic Indicators," NBER Working Papers 2772, National Bureau of Economic Research, Inc.
    20. Francis X. Diebold & Glenn D. Rudebusch, 1989. "Forecasting output with the composite leading index: an ex ante analysis," Finance and Economics Discussion Series 90, Board of Governors of the Federal Reserve System (U.S.).
    21. 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.
    22. Croushore, Dean, 2005. "Do consumer-confidence indexes help forecast consumer spending in real time?," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 435-450, December.
    Full references (including those not matched with items on IDEAS)

    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. Boriss Siliverstovs, 2013. "Do business tendency surveys help in forecasting employment?: A real-time evidence for Switzerland," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 129-151.
    2. Lemmens, Aurélie & Croux, Christophe & Dekimpe, Marnik G., 2008. "Measuring and testing Granger causality over the spectrum: An application to European production expectation surveys," International Journal of Forecasting, Elsevier, vol. 24(3), pages 414-431.
    3. Mansoor Maitah & Daniel Toth & Elena Kuzmenko & Karel r dl & Helena Rezbov & Petra nov, 2016. "Forecast of Employment in Switzerland: The Macroeconomic View," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 132-138.
    4. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    5. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    6. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    7. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    8. 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.
    9. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    10. 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.
    11. Lemmens, A. & Croux, C. & Dekimpe, M.G., 2004. "Decomposing Granger Causality over the Spectrum," ERIM Report Series Research in Management ERS-2004-102-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    12. Lui, Silvia & Mitchell, James & Weale, Martin, 2011. "The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1128-1146, October.
    13. Tatiana Cesaroni & Stefano Iezzi, 2017. "The Predictive Content of Business Survey Indicators: Evidence from SIGE," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 75-104, May.
    14. Juan G Brida & Bibiana Lanzilotta & Lucia I Rosich, 2021. "On the empirical relations between producers expectations and economic growth," Economics Bulletin, AccessEcon, vol. 41(3), pages 1970-1982.
    15. Klein, Lawrence R. & Özmucur, Süleyman, 2010. "The use of consumer and business surveys in forecasting," Economic Modelling, Elsevier, vol. 27(6), pages 1453-1462, November.
    16. Vermeulen, Philip, 2014. "An evaluation of business survey indices for short-term forecasting: Balance method versus Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 30(4), pages 882-897.
    17. Hansson, Jesper & Jansson, Per & Löf, Mårten, 2003. "Business Survey Data: Do They Help in Forecasting the Macro Economy?," Working Papers 84, National Institute of Economic Research.
    18. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
    19. Andrzej Cieslik & Mahdi Ghodsi, 2021. "Economic sentiment indicators and foreign direct investment: Empirical evidence from European Union countries," International Economics, CEPII research center, issue 168, pages 56-75.
    20. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.

    More about this item

    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:kof:wpskof:10-249. 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/koethch.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.