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In Search of Leading Indicators of Economic Activity in Germany

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  • Harm Bandholz
  • Michael Funke

Abstract

In this Paper we present two new composite leading indicators of economic activity in Germany estimated using a dynamic factor model with and without regime switching. The obtained optimal inferences of business cycle turning points indicate that the two-state regime switching procedure leads to a successful representation of the sample data and provides an appropriate tool for forecasting business conditions.

Suggested Citation

  • Harm Bandholz & Michael Funke, 2003. "In Search of Leading Indicators of Economic Activity in Germany," Quantitative Macroeconomics Working Papers 20307, Hamburg University, Department of Economics.
    • Handle: RePEc:ham:qmwops:20307
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    1. Stefan Gerlach & Matthew S. Yiu, 2004. "A Dynamic Factor Model for Current-Quarter Estimates of Economic Activity in Hong Kong," Working Papers 162004, Hong Kong Institute for Monetary Research.
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    3. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    4. 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.
    5. Burcu Gurcihan Yunculer & Gonul Sengul & Arzu Yavuz, 2014. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 14(1), pages 23-45.
    6. Benjamin Beckers & Konstantin A. Kholodilin & Dirk Ulbricht, 2017. "Reading between the Lines: Using Media to Improve German Inflation Forecasts," Discussion Papers of DIW Berlin 1665, DIW Berlin, German Institute for Economic Research.
    7. Čižmešija Mirjana & Sorić Petar & Lolić Ivana, 2017. "The interrelationship between media reports and the recession in Croatia," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 3(1), pages 16-34, June.
    8. Mapa, Dennis S. & Simbulan, Maria Christina, 2014. "Analyzing and Forecasting Movements of the Philippine Economy using the Dynamic Factor Models (DFM)," MPRA Paper 54478, University Library of Munich, Germany.
    9. Robert Lehmann, 2020. "The Forecasting Power of the ifo Business Survey," CESifo Working Paper Series 8291, CESifo.
    10. Jonas Dovern & Christina Ziegler, 2008. "Predicting Growth Rates and Recessions. Assessing U.S. Leading Indicators under Real-Time Condition," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 54(4), pages 293-318.
    11. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
    12. Monica Billio & Anna Petronevich, 2017. "Dynamical Interaction between Financial and Business Cycles," Post-Print hal-01692239, HAL.
    13. Heinrich, Markus & Carstensen, Kai & Reif, Magnus & Wolters, Maik, 2017. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168206, Verein für Socialpolitik / German Economic Association.
    14. 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.
    15. Sascha O. Becker & Klaus Wohlrabe, 2008. "European Data Watch: Micro Data at the Ifo Institute for Economic Research – The “Ifo Business Survey”, Usage and Access," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 128(2), pages 307-319.
    16. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    17. Dreger, Christian & Schumacher, Christian, 2002. "Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models?," Discussion Paper Series 26321, Hamburg Institute of International Economics.
    18. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    19. Shirly Siew-Ling WONG & Chin-Hong PUAH & Shazali ABU MANSOR & Venus Khim-Sen LIEW, 2016. "Measuring Business Cycle Fluctuations: An Alternative Precursor To Economic Crises," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(4), pages 235-248.
    20. Michael Graff, 2006. "Ein multisektoraler Sammelindikator für die Schweizer Konjunktur," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 142(IV), pages 529-577, December.
    21. Harm Bandholz, 2005. "New Composite Leading Indicators for Hungary and Poland," ifo Working Paper Series 3, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    22. Chen, Shyh-Wei & Shen, Chung-Hua, 2006. "When Wall Street conflicts with Main Street--The divergent movements of Taiwan's leading indicators," International Journal of Forecasting, Elsevier, vol. 22(2), pages 317-339.
    23. Brooks, Chris & Fernandez-Perez, Adrian & Miffre, Joëlle & Nneji, Ogonna, 2016. "Commodity risks and the cross-section of equity returns," The British Accounting Review, Elsevier, vol. 48(2), pages 134-150.

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