IDEAS home Printed from https://ideas.repec.org/p/cnf/wpaper/0804.html
   My bibliography  Save this paper

Business Cycles in the Euro Area Defined with Coincident Economic Indicators and Predicted with Leading Economic Indicators

Author

Listed:
  • Ataman Ozyildirim

    (The Conference Board)

  • Brian Schaitkin

    (The Conference Board)

  • Victor Zarnowitz

    (The Conference Board)

Abstract

Clusters of cyclical turning points in the coincident indicators help us identify and date Euro Area recessions and recoveries in the past several decades. In the U.S. and some other countries, composite indexes of coincident indicators (CEI) are used to date classical business cycle turning points; also indexes of leading indicators (LEI) are used to help in the difficult task of predicting these turning points. This paper reviews a selection of the available data for monthly and quarterly Euro Area coincident and leading indicators. From these data, we develop composite indexes using methods analogous to those tested in the U.S. CEI and LEI published by The Conference Board. We compare the resulting business cycle chronology with the existing alternatives and evaluate our selection of leading indicators in the context of how well they predict current economic activity and its major fluctuations for the Euro Area.

Suggested Citation

  • Ataman Ozyildirim & Brian Schaitkin & Victor Zarnowitz, 2008. "Business Cycles in the Euro Area Defined with Coincident Economic Indicators and Predicted with Leading Economic Indicators," Economics Program Working Papers 08-04, The Conference Board, Economics Program.
  • Handle: RePEc:cnf:wpaper:0804
    as

    Download full text from publisher

    File URL: http://www.conference-board.org/economics/workingpapers.cfm?pdf=E-0031-08-WP
    File Function: First version, 2008
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Chao, John & Corradi, Valentina & Swanson, Norman R., 2001. "Out-Of-Sample Tests For Granger Causality," Macroeconomic Dynamics, Cambridge University Press, vol. 5(04), pages 598-620, September.
    2. Corradi, Valentina & Swanson, Norman R., 2002. "A consistent test for nonlinear out of sample predictive accuracy," Journal of Econometrics, Elsevier, vol. 110(2), pages 353-381, October.
    3. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, pages 1545-1578.
    4. S. Boragan Aruoba, 2008. "Data Revisions Are Not Well Behaved," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2-3), pages 319-340, March.
    5. Zarnowitz, Victor & Ozyildirim, Ataman, 2006. "Time series decomposition and measurement of business cycles, trends and growth cycles," Journal of Monetary Economics, Elsevier, pages 1717-1739.
    6. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, pages 85-110.
    7. Bouwman, Kees E. & Jacobs, Jan P.A.M., 2011. "Forecasting with real-time macroeconomic data: The ragged-edge problem and revisions," Journal of Macroeconomics, Elsevier, pages 784-792.
    8. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    9. McGuckin, Robert H. & Ozyildirim, Ataman & Zarnowitz, Victor, 2007. "A More Timely and Useful Index of Leading Indicators," Journal of Business & Economic Statistics, American Statistical Association, pages 110-120.
    10. repec:dgr:rugccs:200505 is not listed on IDEAS
    11. Uhlig, H.F.H.V.S. & Ravn, M., 1997. "On Adjusting the H-P Filter for the Frequency of Observations," Discussion Paper 1997-50, Tilburg University, Center for Economic Research.
    12. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    13. 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.
    14. Maximo Camacho & Gabriel Perez-Quiros, 2002. "This is what the leading indicators lead," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(1), pages 61-80.
    15. Robert Inklaar & Jan Jacobs & Ward Romp, 2005. "Business Cycle Indexes: Does a Heap of Data Help?," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(3), pages 309-336.
    16. 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, January.
    17. 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.
    18. Fagan, Gabriel & Henry, Jérôme & Mestre, Ricardo, 2001. "An area-wide model (AWM) for the euro area," Working Paper Series 0042, European Central Bank.
    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. Donadelli, Michael & Paradiso, Antonio & Riedel, Max, 2016. "A quasi real-time leading indicator for the EU industrial production," SAFE Working Paper Series 118 [rev.], Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    2. Petr Rozmahel & Ladislava Grochová & Marek Litzman, 2014. "The effect of asymmetries in fiscal policy conducts on business cycle correlation in the EU," WWWforEurope Working Papers series 62, WWWforEurope.
    3. Carstensen Kai & Wohlrabe Klaus & Ziegler Christina, 2011. "Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, pages 82-106.
    4. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2011. "Real-time macroeconomic forecasting with leading indicators: An empirical comparison," International Journal of Forecasting, Elsevier, pages 466-481.
    5. Dries, Liesbeth & Ciaian, Pavel & Kancs, d’Artis, 2012. "Job creation and job destruction in EU agriculture," Food Policy, Elsevier, pages 600-608.
    6. Smimou, K. & Khallouli, W., 2015. "Does the Euro affect the dynamic relation between stock market liquidity and the business cycle?," Emerging Markets Review, Elsevier, vol. 25(C), pages 125-153.
    7. Anna Pestova, 2015. "Leading Indicators of the Business Cycle: Dynamic Logit Models for OECD Countries and Russia," HSE Working papers WP BRP 94/EC/2015, National Research University Higher School of Economics.
    8. Donadelli, Michael & Paradiso, Antonio & Riedel, Max, 2015. "A novel ex-ante leading indicator for the EU industrial production," SAFE Working Paper Series 118, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    9. Heij, C. & van Dijk, D.J.C. & Groenen, P.J.F., 2009. "Macroeconomic forecasting with real-time data: an empirical comparison," Econometric Institute Research Papers EI 2009-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. Scarpel, Rodrigo Arnaldo, 2014. "A demand trend change early warning forecast model for the city of São Paulo multi-airport system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 65(C), pages 23-32.

    More about this item

    Keywords

    Business Cycle; Indicators; Leading Index; Times Series; Forecasting;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:cnf:wpaper:0804. 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: (A Ozyildirim). General contact details of provider: http://edirc.repec.org/data/confbus.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.