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A PMI-Based Real GDP Tracker for the Euro Area

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  • Gabe J. Bondt

    (European Central Bank)

Abstract

Real-time evidence for the euro area shows that a tracker for real GDP growth using only the Purchasing Managers’ Index (PMI) composite output is of similar accuracy for the final GDP release as the first GDP release. No signs of instability—except during the 2008/09 crisis—in this tracking performance are found. This is surprising given the small size of the underlying PMI panel. From a closer look at what is driving this outstanding track record, seven conclusions emerge: (i) the level of and change in the PMI composite output explain one-third of the GDP revisions; (ii) later available information is more accurate; (iii) services are key; (iv) firm size breakdown is valuable; (v) export status breakdown creates only noise; (vi) aggregated euro area PMI track record is not consistently related to a particular country; (vii) take firm defaults into account during very bad times. These findings imply that PMI surveys are not only valuable for analysts and policymakers as a timely and reliable GDP tracker, but also for statisticians to potentially improve the accuracy of the first preliminary flash estimate of euro area real GDP.

Suggested Citation

  • Gabe J. Bondt, 2019. "A PMI-Based Real GDP Tracker for the Euro Area," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 15(2), pages 147-170, December.
    • Handle: RePEc:spr:jbuscr:v:15:y:2019:i:2:d:10.1007_s41549-018-0032-2
    DOI: 10.1007/s41549-018-0032-2
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    References listed on IDEAS

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    1. Chudik, Alexander & Grossman, Valerie & Pesaran, M. Hashem, 2016. "A multi-country approach to forecasting output growth using PMIs," Journal of Econometrics, Elsevier, vol. 192(2), pages 349-365.
    2. Lahiri, Kajal & Monokroussos, George, 2013. "Nowcasting US GDP: The role of ISM business surveys," International Journal of Forecasting, Elsevier, vol. 29(4), pages 644-658.
    3. Raïsa Basselier & David Antonio Liedo & Geert Langenus, 2018. "Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 1-46, April.
    4. 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.
    5. Schnatz, Bernd & D'Agostino, Antonello, 2012. "Survey-based nowcasting of US growth: a real-time forecast comparison over more than 40 years," Working Paper Series 1455, European Central Bank.
    6. Maximo Camacho & Agustin Garcia‐Serrador, 2014. "The Euro‐Sting Revisited: The Usefulness of Financial Indicators to Obtain Euro Area GDP Forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 186-197, April.
    7. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    8. de Bondt Gabe Jacob & Kosekova Stanimira Vasileva, 2018. "Euro Area Growth Signals from Industrial Production: Warnings from a Comparison of Gross Value Added and Production," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 238(6), pages 477-500, October.
    9. 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.
    10. Elena Angelini & Marta Banbura & Gerhard Rünstler, 2010. "Estimating and forecasting the euro area monthly national accounts from a dynamic factor model," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(1), pages 1-22.
    11. Alexander Chudik & George Kapetanios & M. Hashem Pesaran, 2016. "Big Data Analytics: A New Perspective," CESifo Working Paper Series 5824, CESifo.
    12. Graham Elliott & Allan Timmermann, 2016. "Forecasting in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 81-110, October.
    13. Hansen, Bruce E, 1997. "Approximate Asymptotic P Values for Structural-Change Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 60-67, January.
    14. YiLi Chien & Paul Morris, 2016. "PMI and GDP: Do They Correlate for the United States? For China?," Economic Synopses, Federal Reserve Bank of St. Louis, issue 6, pages 1-2.
    15. Alessandro Girardi & Christian Gayer & Andreas Reuter, 2016. "The Role of Survey Data in Nowcasting Euro Area GDP Growth," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(5), pages 400-418, August.
    16. Gajewski Paweł, 2014. "Nowcasting Quarterly GDP Dynamics in the Euro Area – The Role of Sentiment Indicators," Comparative Economic Research, Sciendo, vol. 17(2), pages 1-19, June.
    17. 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.
    18. Gabe J. Bondt & Stefano Schiaffi, 2015. "Confidence Matters for Current Economic Growth: Empirical Evidence for the Euro Area and the United States," Social Science Quarterly, Southwestern Social Science Association, vol. 96(4), pages 1027-1040, December.
    19. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
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    Cited by:

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    2. Stefan Sauer & Klaus Wohlrabe, 2024. "What Is Behind the ifo Business Climate? Evidence from a Meta-Survey," CESifo Working Paper Series 11482, CESifo.
    3. Yifei Li & Yuhang Bai, 2023. "Research on the Impact of Global Economic Policy Uncertainty on Manufacturing: Evidence from China, the United States, and the European Union," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    4. Michael Berlemann & Vera Jahn & Robert Lehmann, 2022. "Is the German Mittelstand more resistant to crises?," Small Business Economics, Springer, vol. 59(3), pages 1169-1195, October.
    5. Robert Lehmann & Magnus Reif, 2021. "Predicting the German Economy: Headline Survey Indices Under Test," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 215-232, November.

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    More about this item

    Keywords

    GDP nowcasting; Survey indicators; Real-time analysis; GDP revisions; Euro area;
    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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