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Forecasting economic activity in Germany: how useful are sentiment indicators?

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  • Schröder, Michael
  • Hüfner, Felix P.

Abstract

We analyze four economic sentiment indicators for the German economy regarding their ability to forecast economic activity. Using cross correlations and Granger causality tests we find that the ifo business expectations (ifo), the Purchasing Managers Index (PMI) and the ZEW Indicator of Economic Sentiment (ZEW) lead the yearon-year growth rate of German industrial production by five months. Taking into account the publication lag of industrial production this lead is even larger. On the contrary, the European Commission?s Economic Sentiment Indicator (ESIN) does not exhibit a lead but rather seems to coincide or even lag economic activity. Analyzing lead/lag structures among the indicators we find that the ZEW indicator leads the ifo business expectations significantly by one month and that the latter has a onemonth lead over the PMI. Out-of-sample forecast evaluations suggest that both ifo and ZEW provide the best forecasts for industrial production among the three indicators ifo, PMI and ZEW. It is found that the ZEW indicator performs better than the ifo and PMI over the whole sample (Jan. 1994 – Mar. 2002) and especially over horizons from six to twelve months. The ifo expectations predict better at shorter horizons (up to three months) and is superior to the ZEW and PMI indicator when a shorter sample (Jan. 1998 – Mar. 2002) is regarded.

Suggested Citation

  • Schröder, Michael & Hüfner, Felix P., 2002. "Forecasting economic activity in Germany: how useful are sentiment indicators?," ZEW Discussion Papers 02-56, ZEW - Leibniz Centre for European Economic Research.
    • Handle: RePEc:zbw:zewdip:566
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    Cited by:

    1. Michael J. Lamla & Sarah M. Lein & Jan-Egbert Sturm, 2020. "Media reporting and business cycles: empirical evidence based on news data," Empirical Economics, Springer, vol. 59(3), pages 1085-1105, September.
    2. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    3. Hüfner, Felix P. & Lahl, David, 2003. "What Determines the ZEW Indicator?," ZEW Discussion Papers 03-48, ZEW - Leibniz Centre for European Economic Research.
    4. Maria Antoinette Silgoner, 2005. "An Overview of European Economic Indicators: Great Variety of Data on the Euro Area, Need for More Extensive Coverage of the New EU Member States," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 3, pages 66-89.
    5. Radoslaw Sobko & Maria Klonowska-Matynia, 2021. "The Relationship between the Purchasing Managers’ Index (PMI) and Economic Growth: The Case for Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 198-219.
    6. Michael J. Lamla & Sarah M. Lein & Jan-Egbert Sturm, 2007. "News and Sectoral Comovement," KOF Working papers 07-183, KOF Swiss Economic Institute, ETH Zurich.
    7. Osterloh, Steffen, 2018. "How do politics affect economic sentiment? The effects of uncertainty and policy preferences," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181614, Verein für Socialpolitik / German Economic Association.
    8. Niessen-Ruenzi, Alexandra, 2007. "Media coverage and macroeconomic information processing," SFB 649 Discussion Papers 2007-011, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Hess, Dieter & Niessen, Alexandra, 2007. "The early news catches the attention: On the relative price impact of similar economic indicators," CFR Working Papers 07-03, University of Cologne, Centre for Financial Research (CFR).
    10. Brückbauer Frank & Schröder Michael, 2023. "The ZEW Financial Market Survey Panel," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 243(3-4), pages 451-469, June.
    11. Rossi, José Luiz J. & Laban, Sílvio A. Neto & Claro, Danny Pimentel & Bolzani, Luciana Corrêa, 2009. "Índice de Confiança do Empresário de Pequenos e Médios Negócios no Brasil (IC-PMN): Desenvolvimento e Consolidação," Insper Working Papers wpe_191, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    12. Claro, Danny P & Júnior, José L. R. & Laban Neto, Sílvio A. & Lucci, Cíntia R. & Bolzani, Luciana C. & Carvalho, Marina D. de, 2009. "Índice de Confiança do Empresário de Pequenos e Médios Negócios no Brasil (IC-PMN): Metodologia e Resultados Preliminares," Insper Working Papers wpe_158, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    13. repec:hum:wpaper:sfb649dp2007-011 is not listed on IDEAS
    14. Jan Jacobs & Jan-Egbert Sturm, 2005. "Do Ifo Indicators Help Explain Revisions in German Industrial Production?," Contributions to Economics, in: Jan-Egbert Sturm & Timo Wollmershäuser (ed.), Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pages 93-114, Springer.
    15. Petar Sorić & Ivana Lolić & Mirjana Čižmešija, 2015. "European economic sentiment indicator: An empirical reappraisal," EFZG Working Papers Series 1505, Faculty of Economics and Business, University of Zagreb.
    16. Konstantin A. Kholodilin & Christian Kolmer & Tobias Thomas & Dirk Ulbricht, 2015. "Asymmetric Perceptions of the Economy: Media, Firms, Consumers, and Experts," Discussion Papers of DIW Berlin 1490, DIW Berlin, German Institute for Economic Research.

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    More about this item

    Keywords

    leading indicators; Germany; ifo; zew; PMI; ESIN;
    All these keywords.

    JEL classification:

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

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