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The role of component-wise boosting for regional economic forecasting

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  • Lehmann, Robert
  • Wohlrabe, Klaus

Abstract

This paper applies component-wise boosting to the topic of regional economic forecasting. By using unique quarterly gross domestic product data for one German state for the period from 1996 to 2013, in combination with a large data set of 253 monthly indicators, we show how accurate forecasts obtained from component-wise boosting are compared to a simple benchmark model. We additionally take a closer look into the algorithm and evaluate whether a stable pattern of selected indicators exists over time and four different forecasting horizons. All in all, boosting is a viable method for forecasting regional GDP, especially one and two quarters ahead. We also find that regional survey results, indicators that mirror the Saxon economy and the Composite Leading Indicators by the OECD, get frequently selected by the algorithm.

Suggested Citation

  • Lehmann, Robert & Wohlrabe, Klaus, 2015. "The role of component-wise boosting for regional economic forecasting," MPRA Paper 68186, University Library of Munich, Germany, revised 03 Dec 2015.
    • Handle: RePEc:pra:mprapa:68186
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
    12. Buchen, Teresa & Wohlrabe, Klaus, 2011. "Forecasting with many predictors: Is boosting a viable alternative?," Economics Letters, Elsevier, vol. 113(1), pages 16-18, October.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. repec:ces:ifodic:v:7:y:2009:i:4:p:14567043 is not listed on IDEAS
    18. 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.
    19. 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.
    20. 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).
    Full references (including those not matched with items on IDEAS)

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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. 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.
    4. 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

    Keywords

    boosting; regional economic forecasting; gross domestic product;
    All these keywords.

    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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