Letters in Spatial and Resource Sciences

, Volume 10, Issue 2, pp 161–175 | Cite as

Boosting and regional economic forecasting: the case of Germany

  • Robert LehmannEmail author
  • Klaus Wohlrabe
Original Paper


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.


Boosting Regional economic forecasting Gross domestic product 

JEL Classification

C53 E17 E37 R11 



We thank two anonymous referees as well as Udo Ludwig for very helpful comments and Lisa Giani Contini for editing this text.


  1. Andrada-Félix, J., Fernández-Rodríguez, F.: Improving moving average trading rules with boosting and statistical learning methods. J. Forecast. 27(5), 433–449 (2008)CrossRefGoogle Scholar
  2. Bai, J., Ng, S.: Boosting diffusion indices. J. Appl. Econ. 24(4), 607–629 (2009)CrossRefGoogle Scholar
  3. Brautzsch, H.U., Ludwig, U.: Vierteljährliche Entstehungsrechnung des Bruttoinlandsprodukts für Ostdeutschland: Sektorale Bruttowertschöpfung. IWH-Diskussionspapier No. 164 (2002)Google Scholar
  4. Buchen, T., Wohlrabe, K.: Forecasting with many predictors: is boosting a viable alternative? Econ. Lett. 113(1), 16–18 (2011)CrossRefGoogle Scholar
  5. Buchen, T., Wohlrabe, K.: Assessing the macroeconomic forecasting performance of boosting—evidence for the United States, the Euro area, and Germany. J. Forecast. 33(4), 231–242 (2014)CrossRefGoogle Scholar
  6. Bühlmann, P.: Boosting for high-dimensional linear models. Ann. Stat. 34(2), 559–583 (2006)CrossRefGoogle Scholar
  7. Bühlmann, P., Yu, B.: Boosting with the \(L_2\) loss: regression and classification. J. Am. Stat. Assoc. 98(462), 324–339 (2003)CrossRefGoogle Scholar
  8. Chow, G.C., Lin, A.: Best linear unbiased interpolation, distribution and exploration of time series by related series. Rev. Econ. Stat. 53(4), 372–375 (1971)CrossRefGoogle Scholar
  9. Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Ann. Stat. 32(2), 407–499 (2004)CrossRefGoogle Scholar
  10. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)CrossRefGoogle Scholar
  11. Henzel, S.R., Lehmann, R., Wohlrabe, K.: Nowcasting regional GDP: the case of the Free State of Saxony. Rev. Econ. 66(1), 71–98 (2015)Google Scholar
  12. Kim, H.H., Swanson, N.R.: Forecasting financial and macroeconomic variables using data reduction methods: new empirical evidence. J. Econ. 178(2), 352–367 (2014)CrossRefGoogle Scholar
  13. Lehmann, R., Wohlrabe, K.: Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones? Rev. Reg. Res. 34(1), 61–90 (2014a)CrossRefGoogle Scholar
  14. Lehmann, R., Wohlrabe, K.: Regional economic forecasting: state-of-the-art methodology and future challenges. Econ. Bus. Lett. 3(4), 218–231 (2014b)CrossRefGoogle Scholar
  15. Lehmann, R., Wohlrabe, K.: Forecasting GDP at the regional level with many predictors. Ger. Econ. Rev. 16(2), 226–254 (2015)CrossRefGoogle Scholar
  16. Lehmann, R., Wohlrabe, K.: Looking into the black box of boosting: the case of Germany. Appl. Econ. Lett. 23(17), 1229–1233 (2016)CrossRefGoogle Scholar
  17. Nierhaus, W.: Vierteljährliche Volkswirtschaftliche Gesamtrechnungen für Sachsen mit Hilfe temporaler Disaggregation. ifo Dresden berichtet 14(4), 24–36 (2007)Google Scholar
  18. Pierdzioch, C., Risse, M., Rohloff, S.: Forecasting gold-price fluctuations: a real-time boosting approach. Appl. Econ. Lett. 22(1), 46–50 (2015)CrossRefGoogle Scholar
  19. Pierdzioch, C., Risse, M., Rohloff, S.: A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation. Appl. Econ. Lett. 23(5), 347–352 (2016)CrossRefGoogle Scholar
  20. Ragnitz, J.: Fifteen years after: East Germany revisited. CESifo Forum 6(4), 3–6 (2005)Google Scholar
  21. Ragnitz, J.: East Germany today: successes and failures. CESifo DICE Rep. 7(4), 51–58 (2009)Google Scholar
  22. Robinzonov, N., Tutz, G., Hothorn, T.: Boosting techniques for nonlinear time series models. AStA Adv. Stat. Anal. 96(1), 99–122 (2012)CrossRefGoogle Scholar
  23. Vullhorst, U.: Zur indikatorgestützten Berechnung des vierteljährlichen Bruttoinlandsprodukts für Baden-Württemberg. Statistisches Monatsheft Baden-Württemberg 6, 32–35 (2008)Google Scholar
  24. Zeng, J.: Forecasting aggregates with disaggregate variables: does boosting help to select the most relevant predictors? J. Forecast. (2016, forthcoming)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Ifo Center for Business Cycle Analysis and SurveysIfo Institute – Leibniz-Institute for Economic Research at the University of Munich e.V.MunichGermany

Personalised recommendations