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Composite global indicators from survey data: the Global Economic Barometers

Author

Listed:
  • Klaus Abberger

    (ETH Zürich
    CESifo)

  • Michael Graff

    (ETH Zürich)

  • Oliver Müller

    (ETH Zürich)

  • Jan-Egbert Sturm

    (ETH Zürich
    CESifo)

Abstract

This paper presents a coincident and a leading composite monthly indicator for the world business cycle—the Global Economic Barometers. Both target the world’s output growth rate and consist of economic tendency surveys results from many countries around the world. The calculation of these indicators comprises two main stages. The first consists of a variable selection procedure, in which a pre-set correlation threshold and the targeted leads to the reference series are used as selection criteria. In the second stage, the selected variables are combined and transformed into the respective composite indicators, computed as the first partial least squares factor with the reference series as response variable. We analyse the characteristics of the two new indicators in a pseudo real-time setting and demonstrate that both are useful additions to the small number of indicators for the global business cycle published so far. Finally, yet importantly, the Barometers were quick to plunge in the beginning of March 2020 and have since then given a reliable real-time reflection of the economic consequences of the Covid-19 pandemic.

Suggested Citation

  • Klaus Abberger & Michael Graff & Oliver Müller & Jan-Egbert Sturm, 2022. "Composite global indicators from survey data: the Global Economic Barometers," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(3), pages 917-945, August.
    • Handle: RePEc:spr:weltar:v:158:y:2022:i:3:d:10.1007_s10290-021-00449-8
    DOI: 10.1007/s10290-021-00449-8
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    References listed on IDEAS

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    1. Ambler, Steve & Cardia, Emanuela & Zimmermann, Christian, 2004. "International business cycles: What are the facts?," Journal of Monetary Economics, Elsevier, vol. 51(2), pages 257-276, March.
    2. Drechsel, Katja & Giesen, Sebastian & Lindner, Axel, 2014. "Outperforming IMF Forecasts by the Use of Leading Indicators," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100393, Verein für Socialpolitik / German Economic Association.
    3. Schwert, G William, 2002. "Tests for Unit Roots: A Monte Carlo Investigation," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 5-17, January.
    4. Camacho, Maximo & Martinez-Martin, Jaime, 2015. "Monitoring the world business cycle," Economic Modelling, Elsevier, vol. 51(C), pages 617-625.
    5. 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.
    6. Gregory, Allan W & Head, Allen C & Raynauld, Jacques, 1997. "Measuring World Business Cycles," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 38(3), pages 677-701, August.
    7. Graff Michael, 2006. "Internationale Konjunkturverbunde / International Business Cycles," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(4), pages 385-417, August.
    8. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    9. Pablo A. Cuba-Borda & Alexander Mechanick & Andrea Raffo, 2018. "Monitoring the World Economy: A Global Conditions Index," IFDP Notes 2018-06-15, Board of Governors of the Federal Reserve System (U.S.).
    10. Pablo Duarte & Bernd Süssmuth, 2018. "Implementing an Approximate Dynamic Factor Model to Nowcast GDP Using Sensitivity Analysis," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 127-141, April.
    11. 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.
    12. Golinelli, Roberto & Parigi, Giuseppe, 2014. "Tracking world trade and GDP in real time," International Journal of Forecasting, Elsevier, vol. 30(4), pages 847-862.
    13. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2010. "New Eurocoin: Tracking Economic Growth in Real Time," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1024-1034, November.
    14. Stratford, Kate, 2013. "Nowcasting world GDP and trade using global indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 53(3), pages 233-242.
    15. Camba-Méndez, Gonzalo & Kapetanios, George & Papailias, Fotis & Weale, Martin R., 2015. "An automatic leading indicator, variable reduction and variable selection methods using small and large datasets: Forecasting the industrial production growth for euro area economies," Working Paper Series 1773, European Central Bank.
    16. 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.
    17. 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.
    18. Klaus Abberger & Michael Graff & Jan-Egbert Sturm & Oliver Müller & Aloisio Campelo Jr & Anna Carolina Lemos Gouveia, 2020. "The Global Economic Barometers: Composite indicators for the world economy," KOF Working papers 20-471, KOF Swiss Economic Institute, ETH Zurich.
    19. James Rossiter, 2010. "Nowcasting the Global Economy," Discussion Papers 10-12, Bank of Canada.
    20. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    21. J. C. G. Boot & W. Feibes & J. H. C. Lisman, 1967. "Further Methods of Derivation of Quarterly Figures from Annual Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(1), pages 65-75, March.
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    Cited by:

    1. Klaus Abberger & Michael Graff & Oliver Müller & Boriss Siliverstovs, 2023. "Imputing Monthly Values for Quarterly Time Series: An Application Performed with Swiss Business Cycle Data," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(3), pages 241-273, November.
    2. Jordi Pons-Novell & Montserrat Guillen, 2022. "The Autonomous Capacity of the Elderly Population in Spain for Shopping and Preparing Meals," IJERPH, MDPI, vol. 19(22), pages 1-16, November.

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    More about this item

    Keywords

    Business cycles; Composite indicators; Covid-19 pandemic; Leading indicators; Coincident indicators; Partial least squares; Real-time simulations; World economy;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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