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Will the last be the first? Ranking German macroeconomic forecasters based on different criteria

Author

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  • Tim Köhler

    (University of Applied Sciences Merseburg)

  • Jörg Döpke

    (University of Applied Sciences)

Abstract

We rank the quality of German macroeconomic forecasts using various methods for 17 regular annual German economic forecasts from 14 different institutions for the period from 1993 to 2019. Using data for just one year, rankings based on different methods correlate only weakly with each other. Correlations of rankings calculated for two consecutive years and a given method are often relatively low and statistically insignificant. For the total sample, rank correlations between institutions are generally relatively high among different criteria. We report substantial long-run differences in forecasting quality, which are mostly due to distinct average forecast horizons. In the long-run, choosing the criterion to rank the forecasters is of minor importance. Rankings based on recession years and normal periods are similar. The same does hold for rankings based on real-time vs revised data.

Suggested Citation

  • Tim Köhler & Jörg Döpke, 2023. "Will the last be the first? Ranking German macroeconomic forecasters based on different criteria," Empirical Economics, Springer, vol. 64(2), pages 797-832, February.
    • Handle: RePEc:spr:empeco:v:64:y:2023:i:2:d:10.1007_s00181-022-02267-9
    DOI: 10.1007/s00181-022-02267-9
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    References listed on IDEAS

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    1. Ullrich Heilemann & Herman O. Stekler, 2013. "Has The Accuracy of Macroeconomic Forecasts for Germany Improved?," German Economic Review, Verein für Socialpolitik, vol. 14(2), pages 235-253, May.
    2. Tara M. Sinclair & H. O. Stekler & Warren Carnow, 2012. "A new approach for evaluating economic forecasts," Economics Bulletin, AccessEcon, vol. 32(3), pages 2332-2342.
    3. Antonello D’Agostino & Kieran Mcquinn & Karl Whelan, 2012. "Are Some Forecasters Really Better Than Others?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(4), pages 715-732, June.
    4. Ullrich Heilemann & Karsten Müller, 2018. "Wenig Unterschiede – Zur Treffsicherheit Internationaler Prognosen und Prognostiker [Few differences—on the accuracy of international forecasts and forecaster]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 12(3), pages 195-233, December.
    5. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    6. Casey, Eddie, 2020. "Do macroeconomic forecasters use macroeconomics to forecast?," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1439-1453.
    7. Döhrn, Roland, 2015. "Der Prognostiker des Jahres: Ein Zufallsergebnis? Möglichkeiten einer mehrdimensionalen Evaluierung von Konjunkturprognosen," IBES Diskussionsbeiträge 208, University of Duisburg-Essen, Institute of Business and Economic Studie (IBES).
    8. Dovern, Jonas & Jannsen, Nils, 2017. "Systematic errors in growth expectations over the business cycle," International Journal of Forecasting, Elsevier, vol. 33(4), pages 760-769.
    9. Batchelor, R A, 1990. "All Forecasters Are Equal," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 143-144, January.
    10. Clements, Michael P., 2019. "Do forecasters target first or later releases of national accounts data?," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1240-1249.
    11. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    12. Constantin Bürgi & Tara M. Sinclair, 2017. "A nonparametric approach to identifying a subset of forecasters that outperforms the simple average," Empirical Economics, Springer, vol. 53(1), pages 101-115, August.
    13. Rybinski, Krzysztof, 2021. "Ranking professional forecasters by the predictive power of their narratives," International Journal of Forecasting, Elsevier, vol. 37(1), pages 186-204.
    14. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    15. Timmermann, Allan & Qu, Ritong & Zhu, Yinchu, 2019. "Do Any Economists Have Superior Forecasting Skills?," CEPR Discussion Papers 14112, C.E.P.R. Discussion Papers.
    16. Gamber, Edward N. & Smith, Julie K. & McNamara, Dylan C., 2014. "Where is the Fed in the distribution of forecasters?," Journal of Policy Modeling, Elsevier, vol. 36(2), pages 296-312.
    17. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    18. Timmermann, Allan & Zhu, Yinchu, 2019. "Comparing Forecasting Performance with Panel Data," CEPR Discussion Papers 13746, C.E.P.R. Discussion Papers.
    19. Knüppel, Malte & Vladu, Andreea L., 2016. "Approximating fixed-horizon forecasts using fixed-event forecasts," Discussion Papers 28/2016, Deutsche Bundesbank.
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