IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v42y2010i21p2759-2781.html
   My bibliography  Save this article

Does a multi-sectoral design improve indicator-based forecasts of the GDP growth rate? Evidence from Switzerland

Author

Listed:
  • Michael Graff

Abstract

This article presents a multi-sectoral composite indicator for the Swiss GDP growth rate, targeting a lead of two quarters. The in-sample period ranges from 1991 to 2002 and 14 data points are reserved as out of sample to assess the forecasting performance. The results appear promising, in terms of both phase and amplitude. Comparisons with two other uni-sectoral composite leading indicators for the same reference series-the traditional KOF (Konjunkturforschungsstelle) barometer as published until March 2006 and a uni-sectoral composite indicator computed from the same indicators as the multi-sectoral instrument-show that the new approach is superior to the alternatives, which is due to both its broader information basis as well as to the structure that is imposed by the multi-sectoral design. Yet, there are pronounced differences regarding the accuracy of the sectoral forecasts, so that there is scope for improvement.

Suggested Citation

  • Michael Graff, 2010. "Does a multi-sectoral design improve indicator-based forecasts of the GDP growth rate? Evidence from Switzerland," Applied Economics, Taylor & Francis Journals, vol. 42(21), pages 2759-2781.
    • Handle: RePEc:taf:applec:v:42:y:2010:i:21:p:2759-2781
    DOI: 10.1080/00036840801964641
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/00036840801964641
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036840801964641?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
    2. Mr. Michael J. Artis, 1996. "How Accurate Are the Imf's Short-Term Forecasts? Another Examination of the World Economic Outlook," IMF Working Papers 1996/089, International Monetary Fund.
    3. Michael Graff, 2004. "Estimates of the output gap in real time: how well have we been doing?," Reserve Bank of New Zealand Discussion Paper Series DP 2004/04, Reserve Bank of New Zealand.
    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. Abberger, Klaus & Graff, Michael & Siliverstovs, Boriss & Sturm, Jan-Egbert, 2018. "Using rule-based updating procedures to improve the performance of composite indicators," Economic Modelling, Elsevier, vol. 68(C), pages 127-144.
    2. Sorić, Petar & Lolić, Ivana & Claveria, Oscar & Monte, Enric & Torra, Salvador, 2019. "Unemployment expectations: A socio-demographic analysis of the effect of news," Labour Economics, Elsevier, vol. 60(C), pages 64-74.

    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. 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.
    2. Athanasoulis, S. & Shiller, R.J., 1995. "World Income Components: Measuring and Exploting International Risk Sharing Opportunities," Papers 725, Yale - Economic Growth Center.
    3. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    4. Banerjee, Anindya & Marcellino, Massimiliano, 2006. "Are there any reliable leading indicators for US inflation and GDP growth?," International Journal of Forecasting, Elsevier, vol. 22(1), pages 137-151.
    5. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    6. Bharat Trehan, 1986. "Oil prices, exchange rates and the U.S. economy: an empirical investigation," Economic Review, Federal Reserve Bank of San Francisco, issue Fall, pages 25-43.
    7. Fabio H. Nieto & Luis Fernando Melo, 2001. "About a Coincident Index for the State of the Economy," Borradores de Economia 1938, Banco de la Republica.
    8. Michael Artis & Anindya Banerjee & Massimiliano Marcellino, "undated". "Factor forecasts for the UK," Working Papers 203, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Guowei Cui & Vasilis Sarafidis & Takashi Yamagata, 2020. "IV Estimation of Spatial Dynamic Panels with Interactive Effects: Large Sample Theory and an Application on Bank Attitude," Monash Econometrics and Business Statistics Working Papers 11/20, Monash University, Department of Econometrics and Business Statistics.
    10. Verbeek, Jos, 1999. "The World Bank's Unified Survey projections : how accurate are they? an ex-post evaluation of US91-US97," Policy Research Working Paper Series 2071, The World Bank.
    11. Neely, Christopher J. & Rapach, David E., 2011. "International comovements in inflation rates and country characteristics," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1471-1490.
    12. Dickhaus, Thorsten & Sirotko-Sibirskaya, Natalia, 2019. "Simultaneous statistical inference in dynamic factor models: Chi-square approximation and model-based bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 30-46.
    13. Hiroshi Iyetomi & Yasuhiro Nakayama & Hiroshi Yoshikawa & Hideaki Aoyama & Yoshi Fujiwara & Yuichi Ikeda & Wataru Souma, 2009. "What Causes Business Cycles? Analysis of the Japanese Industrial Production Data," Papers 0912.0857, arXiv.org, revised Nov 2010.
    14. Nii Ayi Armah & Norman Swanson, 2010. "Seeing Inside the Black Box: Using Diffusion Index Methodology to Construct Factor Proxies in Large Scale Macroeconomic Time Series Environments," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 476-510.
    15. Franco Peracchi & Claudio Rossetti, 2022. "A nonlinear dynamic factor model of health and medical treatment," Health Economics, John Wiley & Sons, Ltd., vol. 31(6), pages 1046-1066, June.
    16. Uebele, Martin, 2011. "National and international market integration in the 19th century: Evidence from comovement," Explorations in Economic History, Elsevier, vol. 48(2), pages 226-242, April.
    17. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
    18. Necati Tekatli, 2007. "Generalized Factor Models: A Bayesian Approach," Working Papers 334, Barcelona School of Economics.
    19. Pavel Vidal Alejandro & Lya Paola Sierra Suárez & Johana Sanabria Dominguez & Jaime Andres Collazos Rodríguez, 2015. "Indicador mensual de actividad económica (IMAE) para el Valle del Cauca," Borradores de Economia 900, Banco de la Republica de Colombia.
    20. Masahiro Ashiya, 2003. "The directional accuracy of 15-months-ahead forecasts made by the IMF," Applied Economics Letters, Taylor & Francis Journals, vol. 10(6), pages 331-333.

    More about this item

    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:taf:applec:v:42:y:2010:i:21:p:2759-2781. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    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.