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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 - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  • Handle: RePEc:zbw:zewdip:566
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    1. Ulrich Fritsche, 1999. "Vorlaufeigenschaften von Ifo-Indikatoren für Westdeutschland," Discussion Papers of DIW Berlin 179, DIW Berlin, German Institute for Economic Research.
    2. Norbert Funke, 1997. "Predicting recessions: Some evidence for Germany," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 133(1), pages 90-102, March.
    3. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," Review of Economic Studies, Oxford University Press, vol. 61(4), pages 631-653.
    4. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    5. Ulrich Fritsche & Sabine Stephan, 2000. "Leading Indicators of German Business Cycles: An Assessment of Properties," Macroeconomics 0004005, EconWPA.
    6. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    7. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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    Cited by:

    1. 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.
    2. Hüfner, Felix P. & Lahl, David, 2003. "What Determines the ZEW Indicator?," ZEW Discussion Papers 03-48, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    3. Jan Jacobs & Jan-Egbert Sturm, 2004. "Do Ifo Indicators Help Explain Revisions in German Industrial Production?," CESifo Working Paper Series 1205, CESifo Group Munich.
    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. Konstantin 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.
    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. 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.
    8. 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.

    More about this item

    Keywords

    leading indicators; Germany; ifo; zew; PMI; ESIN;

    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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