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Composite Leading Indicators for Major OECD Non-Member Economies: Brazil, China, India, Indonesia, Russian Federation, South Africa

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  • Ronny Nilsson

    (OECD)

  • Olivier Brunet

    (OECD)

Abstract

The OECD developed a System of Composite Leading indicators for its Member countries in the early 1980's based on the 'growth cycle' approach. Today the OECD compiles composite leading indicators (CLIs) for 23 of its 30 Member countries and it is envisaged to expand country coverage to include all Member countries and the major six OECD non-member economies (NMEs) monitored by the organization in the OECD System of Composite Leading Indicators. The importance of the six major NMEs was considered the first priority and a workshop with participants from the six major NMEs was held at the OECD in Paris in April 2005 to discuss an initial OECD selection of potential leading indicators for the six major NMEs and national suggestions for alternative and/or additional potential leading indicators for calculation of country specific composite leading indicators. The outcomes of this meeting and followup activities undertaken by the OECD in co-operation with the participating national agencies are reflected in the results presented in this final version of the document. The OECD indicator system uses univariate analysis to estimate trend and cycles individually for each component series and then a composite indicator is obtained by aggregation of the resulting de-trended components. Today, statistical techniques based on alternative univariate methods and multivariate analysis are increasingly used in cyclical analysis and some of these techniques are used in this study to supplement the current OECD approach in the selection of leading components and the construction of composite indicators. L’OCDE a développé un système d’indicateurs composites avancés pour ses pays membres au début des années 80 basé sur les "cycles de croissance". Aujourd’hui, l’OCDE calcule les indicateurs composites avancés pour 23 des 30 pays membres et envisage d’étendre la couverture du système des indicateurs composites avancés à tous les pays membres ainsi qu’aux six principales économies non membres suivies par l’Organisation. L’importance des six principales économies non membres est considérée comme prioritaire et un séminaire regroupant ces six principales économies non membres fut organisé au siège de l’OCDE à Paris en avril 2005 afin de discuter d’une première sélection par l’OCDE d’indicateurs avancés potentiels pour les six principales économies non membres et discuter des suggestions des pays pour des indicateurs avancés potentiels alternatifs et/ou supplémentaires pour le calcul des indicateurs composites avancés spécifiques aux pays. Les résultats de cette réunion et les futures activités entreprises par l’OCDE en collaboration avec les agences nationales participantes sont décrits dans la version finale de ce document. Le système des indicateurs composites avancés de l’OCDE utilise une analyse univariée afin d’estimer la tendance et les cycles individuellement pour chaque série composante et ensuite un indicateur composite est obtenu par aggrégation des composantes sans tendance. Aujourd’hui, les techniques statistiques basées sur d’autres méthodes d’analyse univariée ainsi que multivariée sont de plus en plus utilisées en analyse cyclique et certaines de ces techniques sont utilisées dans l’étude afin de compléter l’approche courante de l’OCDE dans la sélection des composantes avancées et dans la construction des indicateurs composites.

Suggested Citation

  • Ronny Nilsson & Olivier Brunet, 2006. "Composite Leading Indicators for Major OECD Non-Member Economies: Brazil, China, India, Indonesia, Russian Federation, South Africa," OECD Statistics Working Papers 2006/1, OECD Publishing.
  • Handle: RePEc:oec:stdaaa:2006/1-en
    DOI: 10.1787/834716666802
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    Cited by:

    1. Roberto Casarin & Komla Mawulom Agudze & Monica Billio & Eric Girardin, 2014. "Growth-cycle phases in China�s provinces: A panel Markov-switching approach," Working Papers 2014:19, Department of Economics, University of Venice "Ca' Foscari".
    2. Chen, Qiwei & Costantini, Mauro & Deschamps, Bruno, 2016. "How accurate are professional forecasts in Asia? Evidence from ten countries," International Journal of Forecasting, Elsevier, vol. 32(1), pages 154-167.
    3. repec:zbw:bofitp:2006_006 is not listed on IDEAS
    4. Krzysztof Zalewski, 2009. "Forecasting Turning Points with Composite Leading Indicators - the Case of Poland," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 24.
    5. Bruno Deschamps & Paolo Bianchi, 2012. "An evaluation of Chinese macroeconomic forecasts," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 10(3), pages 229-246, December.
    6. Declan Curran & Michael Funke, 2006. "Taking the Temperature - Forecasting GDP Growth for Mainland China," Quantitative Macroeconomics Working Papers 20606, Hamburg University, Department of Economics.

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