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Freedom of Choice in Macroeconomic Forecasting

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  • Nikolay Robinzonov
  • Klaus Wohlrabe

Abstract

Different studies provide a surprisingly large variety of controversial conclusions about the forecasting power of an indicator, even when it is supposed to forecast the same time series. In this study, we aim to provide a thorough overview of linear forecasting techniques and draw conclusions useful for the identification of the predictive relationship between leading indicators and time series. In a case study for Germany, we forecast two possible representations of industrial production. Further on we consider a large variety of time-varying specifications. In a horse race with nine leading indicators plus an AR benchmark model, we demonstrate the variance of assessment across target variables and forecasting settings (50 per horizon). We show that it is nearly always possible to find situations in which one indicator proved to have better predicting power compared with another. Nevertheless, the freedom of choice can be useful to identify robust leading indicators. (JEL codes: C52, C53, E37) Copyright The Author 2009. Published by Oxford University Press on behalf of Ifo Institute for Economic Research, Munich. All rights reserved. For permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

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  • Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo Group, vol. 56(2), pages 192-220, June.
    • Handle: RePEc:oup:cesifo:v:56:y:2010:i:2:p:192-220
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    Cited by:

    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. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    3. Anna Sophia Ciesielski & Klaus Wohlrabe, 2011. "Sector-based Forecasts in Manufacturing," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 27-35, November.
    4. 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.
    5. Christian Seiler, 2015. "On the robustness of balance statistics with respect to nonresponse," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(2), pages 45-62.
    6. Gerit Vogt, 2009. "Konjunkturprognose in Deutschland. Ein Beitrag zur Prognose der gesamtwirtschaftlichen Entwicklung auf Bundes- und Länderebene," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 36.
    7. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52.
    8. Christian Seiler, 2014. "Mode Preferences in Business Surveys: Evidence from Germany," ifo Working Paper Series 193, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    9. Klaus Wohlrabe, 2011. "Konstruktion von Indikatoren zur Analyse der wirtschaftlichen Aktivität in den Dienstleistungsbereichen," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 55.
    10. Christian Seiler & Klaus Wohlrabe, 2013. "The Ifo Business Climate and the German Economy," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.
    11. Robert Lehmann, 2021. "Forecasting exports across Europe: What are the superior survey indicators?," Empirical Economics, Springer, vol. 60(5), pages 2429-2453, May.
    12. Anna Billharz & Steffen Elstner & Marcus Jüppner, 2012. "Ifo Short-Term Forecasting Methods Illustrated Using Investment in Equipment," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 65(21), pages 24-33, November.
    13. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    14. Nikolaos Askitas & Klaus F. Zimmermann, 2013. "Nowcasting Business Cycles Using Toll Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 299-306, July.
    15. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).
    16. Christian Seiler, 2014. "The determinants of unit non-response in the Ifo Business Survey," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(3), pages 161-177, September.
    17. Klaus Wohlrabe, 2009. "Macroeconomic forecasting with mixed frequencies," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    18. Robert Lehmann & Klaus Wohlrabe, 2012. "Forecast of Gross Domestic Product at a Regional Level," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 65(21), pages 17-23, November.
    19. Robert Lehmann & Klaus Wohlrabe, 2013. "Sektorale Prognosen und deren Machbarkeit auf regionaler Ebene – Das Beispiel Sachsen," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 20(04), pages 22-29, August.
    20. Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    21. Klaus Wohlrabe, 2012. "Forecast for the Services Sector in Germany," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 65(01), pages 31-39, January.
    22. Katja Rietzler & Sabine Stephan, 2012. "Monthly recession predictions in real time: A density forecast approach for German industrial production," IMK Working Paper 94-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.

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    More about this item

    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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