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Nonparametric Regression and the Detection of Turning Points in the Ifo Business Climate

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  • Klaus Abberger

Abstract

Business climate indicators are used to receive early signals for turning points in the general business cycle. Therefore methods for the detection of turning points in time series are required. Estimations of slopes of a smooth component in the data can be calculated with local polynomial regression. A change in the sign of the slope can be interpreted as a turning point. A plug-in method is used for data-based bandwidth choice. Since in practice the identification of turning points at the actual boundary of the time series is of special interest, this situation is discussed in more detail. The nonparametric approach is applied to the Ifo Business Climate to demonstrate the application of the nonparametric approach and to analyze the time lead of the indicator.

Suggested Citation

  • Klaus Abberger, 2004. "Nonparametric Regression and the Detection of Turning Points in the Ifo Business Climate," CESifo Working Paper Series 1283, CESifo.
    • Handle: RePEc:ces:ceswps:_1283
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    File URL: https://www.cesifo.org/DocDL/cesifo1_wp1283.pdf
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    References listed on IDEAS

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    1. Christian Hott & André Kunkel & Gernot Nerb, 2007. "The Accuracy of Turning Point Predictions with the Ifo Business Climate," Chapters, in: Georg Goldrian (ed.), Handbook of Survey-Based Business Cycle Analysis, chapter 14, Edward Elgar Publishing.
    2. Lars-Erik Öller & Lasse Koskinen, 2004. "A classifying procedure for signalling turning points," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 197-214.
    3. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    4. Eva Andersson & David Bock & Marianne Frisén, 2004. "Detection of Turning Points in Business Cycles," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(1), pages 93-108.
    5. Jan Beran & Yuanhua Feng, 2002. "Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(2), pages 291-311, June.
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    Cited by:

    1. Sesma Julio & Husted Bryan & Banks Jerry, 2014. "La medición del desempeño social empresarial a través de las redes sociales," Contaduría y Administración, Accounting and Management, vol. 59(2), pages 121-143, abril-jun.
    2. Franco Mariuzzo & Patrick Paul Walsh & Ciara Whelan, 2004. "EU Merger Control in Differentiated Product Industries," CESifo Working Paper Series 1312, CESifo.
    3. Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
    4. Кокорева Мария Сергеевна & Степанова Анастасия Николаевна, 2012. "Financial architecture and corporate performance: evidence from Russia," Journal of Corporate Finance Research Корпоративные финансы, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», issue 2 (22), pages 34-44.
    5. Abberger, Klaus, 2007. "Qualitative business surveys and the assessment of employment -- A case study for Germany," International Journal of Forecasting, Elsevier, vol. 23(2), pages 249-258.
    6. 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.
    7. Klaus Abberger & Sascha Becker & Barbara Hofmann & Klaus Wohlrabe, 2007. "Mikrodaten im ifo Institut für Wirtschaftsforschung – Bestand, Verwendung und Zugang," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 1(1), pages 27-42, June.
    8. 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.

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    Keywords

    Nonparametric regression; slope estimation; turning points; business climate indicators;
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