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Boosting and regional economic forecasting: the case of Germany

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  • Lehmann, Robert
  • Wohlrabe, Klaus

Abstract

This paper applies component-wise boosting to the topic of regional economic forecasting. Component-wise boosting is a pre-selection algorithm of indicators for forecasting. By using unique quarterly real gross domestic product data for two German states (the Free State of Saxony and Baden-Wuerttemberg) and Eastern Germany for the period from 1997 to 2013, in combination with a large data set of monthly indicators, we show that boosting is generally doing a very good job in regional economic forecasting. We additionally take a closer look into the algorithm and ask which indicators get selected. All in all, boosting outperforms our benchmark model for all the three regions considered. We also find that indicators that mirror the region-specific economy get frequently selected by the algorithm.

Suggested Citation

  • Lehmann, Robert & Wohlrabe, Klaus, 2017. "Boosting and regional economic forecasting: the case of Germany," Munich Reprints in Economics 49919, University of Munich, Department of Economics.
    • Handle: RePEc:lmu:muenar:49919
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    References listed on IDEAS

    as
    1. repec:zbw:iwhdps:164 is not listed on IDEAS
    2. Klaus Wohlrabe & Teresa Buchen, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
    3. Henzel Steffen R. & Wohlrabe Klaus & Lehmann Robert, 2015. "Nowcasting Regional GDP: The Case of the Free State of Saxony," Review of Economics, De Gruyter, vol. 66(1), pages 71-98, April.
    4. Christian Pierdzioch & Marian Risse & Sebastian Rohloff, 2015. "Forecasting gold-price fluctuations: a real-time boosting approach," Applied Economics Letters, Taylor & Francis Journals, vol. 22(1), pages 46-50, January.
    5. R. Lehmann & K. Wohlrabe, 2016. "Looking into the black box of boosting: the case of Germany," Applied Economics Letters, Taylor & Francis Journals, vol. 23(17), pages 1229-1233, November.
    6. Lehmann Robert & Wohlrabe Klaus, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, De Gruyter, vol. 16(2), pages 226-254, May.
    7. Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
    8. Christian Pierdzioch & Marian Risse & Sebastian Rohloff, 2016. "A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation," Applied Economics Letters, Taylor & Francis Journals, vol. 23(5), pages 347-352, March.
    9. Buchen, Teresa & Wohlrabe, Klaus, 2011. "Forecasting with many predictors: Is boosting a viable alternative?," Economics Letters, Elsevier, vol. 113(1), pages 16-18, October.
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    11. Joachim Ragnitz, 2009. "East Germany Today: Successes and Failures," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 7(4), pages 51-58, 01.
    12. Brautzsch, Hans-Ulrich & Ludwig, Udo, 2002. "Vierteljährliche Entstehungsrechnung des Bruttoinlandsprodukts für Ostdeutschland: Sektorale Bruttowertschöpfung," IWH Discussion Papers 164/2002, Halle Institute for Economic Research (IWH).
    13. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    14. Joachim Ragnitz, 2009. "East Germany Today: Successes and Failures," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 7(04), pages 51-58, January.
    15. Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
    16. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    17. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
    18. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
    19. Buhlmann P. & Yu B., 2003. "Boosting With the L2 Loss: Regression and Classification," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 324-339, January.
    20. Joachim Ragnitz, 2005. "Fifteen years after: East Germany revisited," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 6(04), pages 3-6, December.
    21. Wolfgang Nierhaus, 2007. "Vierteljährliche volkswirtschaftliche Gesamtrechnungen für Sachsen mit Hilfe temporaler Disaggregation," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 14(04), pages 24-36, 08.
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    Cited by:

    1. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    2. Lehmann, Robert & Wikman, Ida, 2022. "Quarterly GDP Estimates for the German States," MPRA Paper 112642, University Library of Munich, Germany.
    3. Robert Lehmann & Felix Leiss & Simon Litsche & Stefan Sauer & Michael Weber & Annette Weichselberger & Klaus Wohlrabe, 2019. "Mit den ifo-Umfragen regionale Konjunktur verstehen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(09), pages 45-49, May.
    4. 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.
    5. Guilherme Lindenmeyer & Pedro Pablo Skorin & Hudson da Silva Torrent, 2021. "Using boosting for forecasting electric energy consumption during a recession: a case study for the Brazilian State Rio Grande do Sul," Letters in Spatial and Resource Sciences, Springer, vol. 14(2), pages 111-128, August.
    6. Stefan Sauer & Michael Weber & Klaus Wohlrabe, 2018. "Das neue ifo Geschäftsklima Ostdeutschland und Sachsen: Hintergründe und Anpassungen," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 25(03), pages 20-24, June.

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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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