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Evaluating German business cycle forecasts under an asymmetric loss function

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  • Jörg Döpke

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  • Ulrich Fritsche

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  • Boriss Siliverstovs

    ()

Abstract

Based on annual data for growth and inflation forecasts for Germany covering the 1970-2007 period and up to 17 different forecasts per year, we test for a possible asymmetry of the forecasters’ loss function and estimate the degree of asymmetry for each forecasting institution using the approach of Elliot et al. (2005). Furthermore, we test for the rationality of the forecasts under the assumption of a possibly asymmetric loss function and for the features of an optimal forecast under the assumption of a generalised loss function. We find evidence of the existence of an asymmetric loss function of German forecasters only in the case of pooled data and a quad-quad loss function. We can reject the hypothesis of rationality of the growth forecasts based on a pooled dataset, but not on data for single institutions. The rationality of inflation forecasts is frequently rejected in the case of single institutions, and also for pooled data.

Suggested Citation

  • Jörg Döpke & Ulrich Fritsche & Boriss Siliverstovs, 2010. "Evaluating German business cycle forecasts under an asymmetric loss function," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(1), pages 1-18.
  • Handle: RePEc:oec:stdkab:5kmlj35rx10s
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    File URL: http://dx.doi.org/10.1787/jbcma-2010-5kmlj35rx10s
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    Cited by:

    1. Hans Christian Müller-Dröge & Tara M. Sinclair & H.O. Stekler, 2014. "Evaluating Forecasts of a Vector of Variables: a German Forecasting Competition," CAMA Working Papers 2014-55, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Tsuchiya, Yoichi, 2016. "Asymmetric loss and rationality of Chinese renminbi forecasts: An implication for the trade between China and the US," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 116-127.
    3. Pierdzioch, Christian & Reid, Monique B. & Gupta, Rangan, 2016. "Forecasting the South African inflation rate: On asymmetric loss and forecast rationality," Economic Systems, Elsevier, vol. 40(1), pages 82-92.
    4. Rülke Jan-Christoph, 2012. "Do Private Sector Forecasters Desire to Deviate From the German Council of Economic Experts?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 414-428, August.
    5. Fritsche, Ulrich & Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Forecasting the Brazilian real and the Mexican peso: Asymmetric loss, forecast rationality, and forecaster herding," International Journal of Forecasting, Elsevier, vol. 31(1), pages 130-139.
    6. Claudia M. Buch & Oliver Holtemöller, 2014. "Do we need new modelling approaches in macroeconomics?," Chapters,in: Financial Cycles and the Real Economy, chapter 3, pages 36-58 Edward Elgar Publishing.
    7. Tsuchiya, Yoichi, 2016. "Assessing macroeconomic forecasts for Japan under an asymmetric loss function," International Journal of Forecasting, Elsevier, vol. 32(2), pages 233-242.
    8. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Central banks’ inflation forecasts under asymmetric loss: Evidence from four Latin-American countries," Economics Letters, Elsevier, vol. 129(C), pages 66-70.
    9. repec:eee:ecmode:v:72:y:2018:i:c:p:270-277 is not listed on IDEAS
    10. Tsuchiya, Yoichi, 2012. "Evaluating Japanese corporate executives’ forecasts under an asymmetric loss function," Economics Letters, Elsevier, vol. 116(3), pages 601-603.
    11. Christian Pierdzioch & Jan-Christoph Rülke & Georg Stadtmann, 2013. "Oil price forecasting under asymmetric loss," Applied Economics, Taylor & Francis Journals, vol. 45(17), pages 2371-2379, June.
    12. Jan-Christoph Rülke, 2011. "Do private sector forecasters desire to deviate from the German council of economic experts?," WHU Working Paper Series - Economics Group 11-04, WHU - Otto Beisheim School of Management.
    13. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "On the loss function of the Bank of Canada: A note," Economics Letters, Elsevier, vol. 115(2), pages 155-159.
    14. Jan-Christoph Rülke & Maria Silgoner & Julia Wörz, 2012. "Herding Behavior of Business Cycle Forecasters in Times of Economic Crises," WHU Working Paper Series - Economics Group 12-03, WHU - Otto Beisheim School of Management.
    15. Krüger, Jens J. & Hoss, Julian, 2012. "German business cycle forecasts, asymmetric loss and financial variables," Economics Letters, Elsevier, vol. 114(3), pages 284-287.
    16. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2013. "A note on forecasting the prices of gold and silver: Asymmetric loss and forecast rationality," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(3), pages 294-301.
    17. Jens J. Krüger, 2014. "A multivariate evaluation of German output growth and inflation forecasts," Economics Bulletin, AccessEcon, vol. 34(3), pages 1410-1418.
    18. Jörg Döpke & Ulrich Fritsche & Gabi Waldhof, 2017. "Theories, techniques and the formation of German business cycle forecasts: Evidence from a survey among professional forecasters," Working Papers 2017-002, The George Washington University, Department of Economics, Research Program on Forecasting.
    19. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    20. Jörg Döpke & Ulrich Fritsche & Gabi Waldhof, 2017. "Theories, techniques and the formation of German business cycle forecasts: Evidence from a survey of professional forecasters," Macroeconomics and Finance Series 201701, University of Hamburg, Department of Socioeconomics.

    More about this item

    Keywords

    Business Cycle Forecast Evaluation; Asymmetric Loss Function; Rational Expectations;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System

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