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Forecasting GDP all over the World: Evidence from Comprehensive Survey Data

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  • Garnitz, Johanna
  • Lehmann, Robert
  • Wohlrabe, Klaus

Abstract

Comprehensive and international comparable leading indicators across countries and continents are rare. In this paper, we use a free and fast available source of leading indicators, the World Economic Survey (WES) conducted by the ifo Institute, to forecast growth of Gross Domestic Product (GDP) in 44 countries and three country aggregates separately. We come up with three major results. First, for 35 countries as well as the three aggregates a model containing one of the major WES indicators produces on average lower forecast errors compared to an autoregressive benchmark model. Second, the most important WES indicators are either the economic climate or the expectations on future economic development for the next six months. And last, 70% of all country-specific models contain WES information from at least one of the main trading partners. Thus, by allowing WES indicators from economic important partners to forecast GDP of the country under consideration, increases forecast accuracy.

Suggested Citation

  • Garnitz, Johanna & Lehmann, Robert & Wohlrabe, Klaus, 2017. "Forecasting GDP all over the World: Evidence from Comprehensive Survey Data," MPRA Paper 81772, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:81772
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    1. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    2. Laurent Ferrara & Clément Marsilli, 2019. "Nowcasting global economic growth: A factor‐augmented mixed‐frequency approach," The World Economy, Wiley Blackwell, vol. 42(3), pages 846-875, March.
    3. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    4. Boumans Dorine & Garnitz Johanna, 2017. "Ifo World Economic Survey Database – An International Economic Expert Survey," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 237(1), pages 71-80, February.
    5. Robert Lehmann, 2021. "Forecasting exports across Europe: What are the superior survey indicators?," Empirical Economics, Springer, vol. 60(5), pages 2429-2453, May.
    6. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    7. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    8. Anna Stangl, 2007. "World Economic Survey," Chapters, in: Georg Goldrian (ed.), Handbook of Survey-Based Business Cycle Analysis, chapter 5, Edward Elgar Publishing.
    9. Banerjee, Anindya & Marcellino, Massimiliano, 2006. "Are there any reliable leading indicators for US inflation and GDP growth?," International Journal of Forecasting, Elsevier, vol. 22(1), pages 137-151.
    10. Rongxi Zhou & Xianliang Wang & Guanqun Tong, 2013. "Forecasting macroeconomy based on the term structure of credit spreads: evidence from China," Applied Economics Letters, Taylor & Francis Journals, vol. 20(15), pages 1363-1367, October.
    11. Michail Kiriakidis & Antonios Kargas, 2013. "Greek GDP forecast estimates," Applied Economics Letters, Taylor & Francis Journals, vol. 20(8), pages 767-772, May.
    12. Hutson, Mark & Joutz, Fred & Stekler, Herman, 2014. "Interpreting and evaluating CESIfo's World Economic Survey directional forecasts," Economic Modelling, Elsevier, vol. 38(C), pages 6-11.
    13. Evgenia Kudymowa & Johanna Garnitz & Klaus Wohlrabe, 2014. "Ifo World Economic Survey and the Business Cycle in Selected Countries," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 14(04), pages 51-57, January.
    14. Lahiri, Kajal & Zhao, Yongchen, 2019. "International propagation of shocks: A dynamic factor model using survey forecasts," International Journal of Forecasting, Elsevier, vol. 35(3), pages 929-947.
    15. Duval, Romain & Li, Nan & Saraf, Richa & Seneviratne, Dulani, 2016. "Value-added trade and business cycle synchronization," Journal of International Economics, Elsevier, vol. 99(C), pages 251-262.
    16. Inklaar, Robert & Jong-A-Pin, Richard & de Haan, Jakob, 2008. "Trade and business cycle synchronization in OECD countries--A re-examination," European Economic Review, Elsevier, vol. 52(4), pages 646-666, May.
    17. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," American Economic Review, American Economic Association, vol. 105(8), pages 2644-2678, August.
    18. Jordi Pons-Novell, 2006. "An analysis of a panel of Spanish GDP forecasts," Applied Economics, Taylor & Francis Journals, vol. 38(11), pages 1287-1292.
    19. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    20. 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.
    21. Ferdinand Fichtner & Rasmus Rüffer & Bernd Schnatz, 2011. "The Forecasting Performance of Composite Leading Indicators: Does Globalisation Matter?," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2011(1), pages 55-72.
    22. Oliver Hülsewig & Johannes Mayr & Timo Wollmershäuser, 2008. "Forecasting Euro Area Real GDP: Optimal Pooling of Information," CESifo Working Paper Series 2371, CESifo.
    23. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    24. Steffen Henzel & Timo Wollmershäuser, 2006. "Quantifying Inflation Expectations with the Carlson-Parkin Method: A Survey-based Determination of the Just Noticeable Difference," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 321-352.
    25. 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.
    26. 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.
    27. Christian Seiler & Klaus Wohlrabe, 2013. "Das ifo Geschäftsklima und die deutsche Konjunktur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.
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    Cited by:

    1. Jens J. Krüger, 2021. "A Wavelet Evaluation of Some Leading Business Cycle Indicators for the German Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(3), pages 293-319, December.
    2. Deimante Teresiene & Greta Keliuotyte-Staniuleniene & Yiyi Liao & Rasa Kanapickiene & Ruihui Pu & Siyan Hu & Xiao-Guang Yue, 2021. "The Impact of the COVID-19 Pandemic on Consumer and Business Confidence Indicators," JRFM, MDPI, vol. 14(4), pages 1-23, April.
    3. Abdullah Ghazo, 2021. "Applying the ARIMA Model to the Process of Forecasting GDP and CPI in the Jordanian Economy," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(3), pages 70-77, May.
    4. Dorine Boumans & Clemens Fuest & Carla Krolage & Klaus Wohlrabe, 2020. "Expected effects of the US tax reform on other countries: global and local survey evidence," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 27(6), pages 1608-1630, December.
    5. Jack Fosten & Shaoni Nandi, 2023. "Nowcasting from cross‐sectionally dependent panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 898-919, September.
    6. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    7. María Alejandra Hernández-Montes & Ramón Hernández-Ortega & Jonathan Alexander Muñoz-Martínez, 2022. "Aporte de las expectativas de empresarios al pronóstico de las variables macroeconómicas," Borradores de Economia 1202, Banco de la Republica de Colombia.
    8. Johanna Garnitz & Robert Lehmann & Klaus Wohlrabe, 2019. "Weltweite Prognosen des Bruttoinlandsprodukts mit Hilfe der Indikatoren des ifo World Economic Survey," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(15), pages 36-39, August.
    9. Kleyton da Costa & Felipe Leite Coelho da Silva & Josiane da Silva Cordeiro Coelho & Andr'e de Melo Modenesi, 2020. "A Systematic Comparison of Forecasting for Gross Domestic Product in an Emergent Economy," Papers 2010.13259, arXiv.org, revised Mar 2022.
    10. Dorine Boumans & Henrik Müller & Stefan Sauer, 2022. "How Media Content Influences Economic Expectations: Evidence from a Global Expert Survey," ifo Working Paper Series 380, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    11. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.
    12. Krüger, Jens J., 2024. "A Wavelet Evaluation of Some Leading Business Cycle Indicators for the German Economy," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 149438, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    13. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.

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    More about this item

    Keywords

    World Economic Survey; Economic Climate; Forecasting GDP;
    All these keywords.

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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