IDEAS home Printed from https://ideas.repec.org/p/zbw/iwhdps/iwh-172.html
   My bibliography  Save this paper

Composite Leading Indicators der amerikanischen Wirtschaft - Prognosegüte des Conference Board und des OECD Ansatzes im Vergleich

Author

Listed:
  • Berneburg, Marian

Abstract

The Paper analyses both the Conference Board as well as the OECD Leading Indicators concerning their forecasting properties of overall economic activity. For this purpose the two indicators are introduced separately and several in-sample and out-of-sample tests are being conducted. The main focus, apart from other methods, is being laid on coherence tests as well as the Diebold/Mariano test. In contrast to many other analyses dealing with this topic, the chosen reference series is not industrial production, but rather the coincident index, as reported by the conference board. It seems as if both indicators show some sign of correlation to overall economic activity, but at the same time fail to improve on the forecasts of a simple time series model.

Suggested Citation

  • Berneburg, Marian, 2003. "Composite Leading Indicators der amerikanischen Wirtschaft - Prognosegüte des Conference Board und des OECD Ansatzes im Vergleich," IWH Discussion Papers 172/2003, Halle Institute for Economic Research (IWH).
    • Handle: RePEc:zbw:iwhdps:iwh-172
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/76996/1/172.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John E. Maher, 1957. "Forecasting Industrial Production," Journal of Political Economy, University of Chicago Press, vol. 65(2), pages 158-158.
    2. Davis, E Philip & Fagan, Gabriel, 1997. "Are Financial Spreads Useful Indicators of Future Inflation and Output Growth in EU Countries?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(6), pages 701-714, Nov.-Dec..
    3. Auerbach, Alan J, 1982. "The Index of Leading Indicators: "Measurement without Theory," Thirty-Five Years Later," The Review of Economics and Statistics, MIT Press, vol. 64(4), pages 589-595, November.
    4. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    5. Geoffrey H. Moore & Julius Shiskin, 1967. "Indicators of Business Expansions and Contractions," NBER Books, National Bureau of Economic Research, Inc, number moor67-2.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Gary Gorton, 1982. "Forecasting with the Index of Leading Indicators," Business Review, Federal Reserve Bank of Philadelphia, issue Nov/Dec, pages 15-27.
    8. Wesley Clair Mitchell & Arthur F. Burns, 1938. "Statistical Indicators of Cyclical Revivals," NBER Books, National Bureau of Economic Research, Inc, number mitc38-1.
    9. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    10. Koch, Paul D & Rasche, Robert H, 1988. "An Examination of the Commerce Department Leading-Indicator Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 167-187, April.
    11. Saul H. Hymans, 1973. "On the Use of Leading Indicators to Predict Cyclical Turning Points," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 4(2), pages 339-384.
    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. Fabio Moneta, 2005. "Does the Yield Spread Predict Recessions in the Euro Area?," International Finance, Wiley Blackwell, vol. 8(2), pages 263-301, August.
    2. Brand, Claus & Reimers, Hans-Eggert & Seitz, Franz, 2003. "Forecasting real GDP: what role for narrow money?," Working Paper Series 254, European Central Bank.
    3. 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.
    4. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    5. Keen Meng Choy & Hwee Kwan Chow, 2004. "Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach," Econometric Society 2004 Australasian Meetings 223, Econometric Society.
    6. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2015. "The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US," Applied Economics, Taylor & Francis Journals, vol. 47(22), pages 2259-2277, May.
    7. Sucarrat, Genaro, 2009. "Forecast Evaluation of Explanatory Models of Financial Variability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-33.
    8. Peter Carr & Liuren Wu, 2023. "Decomposing Long Bond Returns: A Decentralized Theory," Review of Finance, European Finance Association, vol. 27(3), pages 997-1026.
    9. Clarida, Richard H. & Sarno, Lucio & Taylor, Mark P. & Valente, Giorgio, 2003. "The out-of-sample success of term structure models as exchange rate predictors: a step beyond," Journal of International Economics, Elsevier, vol. 60(1), pages 61-83, May.
    10. Hamid Baghestani, 2010. "Evaluating Blue Chip forecasts of the trade-weighted dollar exchange rate," Applied Financial Economics, Taylor & Francis Journals, vol. 20(24), pages 1879-1889.
    11. Parigi, Giuseppe & Golinelli, Roberto, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
    12. Yun, Jaeho, 2019. "Bond risk premia in a small open economy with volatile capital flows: The case of Korea," Journal of International Money and Finance, Elsevier, vol. 93(C), pages 223-243.
    13. Wagner Piazza Gaglianone & Jaqueline Terra Moura Marins, 2014. "Risk Assessment of the Brazilian FX Rate," Working Papers Series 344, Central Bank of Brazil, Research Department.
    14. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.
    15. John Kennan, 2001. "Uniqueness of Positive Fixed Points for Increasing Concave Functions on Rn: An Elementary Result," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 4(4), pages 893-899, October.
    16. George B. Tawadros, 2013. "The information content of the Reserve Bank of Australia's inflation forecasts," Applied Economics, Taylor & Francis Journals, vol. 45(5), pages 623-628, February.
    17. Schumacher Christian & Dreger Christian, 2004. "Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models? / Die Schätzung von großen Faktormodellen für die deutsche Volkswirtschaft: Übertreffen sie ei," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 224(6), pages 731-750, December.
    18. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    19. Vogt Gerit, 2007. "Analyse der Prognoseeigenschaften von ifo-Konjunkturindikatoren unter Echtzeitbedingungen / The Forecasting Performance of ifo-indicators Under Real-time Conditions," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 227(1), pages 87-101, February.
    20. Thomas A. Knetsch, 2005. "Evaluating the German Inventory Cycle Using Data from the Ifo Business Survey," Contributions to Economics, in: Jan-Egbert Sturm & Timo Wollmershäuser (ed.), Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pages 61-92, Springer.

    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:zbw:iwhdps:iwh-172. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/iwhhhde.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.