Skip to main content
Log in

Boosting and regional economic forecasting: the case of Germany

  • Original Paper
  • Published:
Letters in Spatial and Resource Sciences Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. Eastern Germany comprises the five German states Brandenburg, Mecklenburg-Western Pomerania, Saxony-Anhalt, the Free State of Saxony and the Free State of Thuringia. The German states equal the NUTS-1-classification of Europe.

  2. The latest data for Saxony are available upon request from dresden@ifo.de.

  3. The latest data for Baden-Wurttemberg are available upon request from vgr@stala.bw.de.

  4. The data can be downloaded free of charge under http://www.iwh-halle.de/c/start/prognose/download.asp?lang=e.

  5. We also experiment with a maximum of \(p=4\), but the resulting forecast errors were larger compared to the ones produced with one lag.

  6. For forecast horizons of three- or four-quarter-ahead (\(h=3,4\)) we found that boosting looses its power compared to the benchmark model.

  7. Cross-validation is an alternative of obtaining the optimal model. However, our small sample prevents us from using this selection criterion.

  8. Nevertheless, other benchmarks are also imaginable, for example, a random walk or a boosted autoregressive process with a higher order than one. Since we implement the boosting algorithm with a maximum number of one lag, the boosted autoregressive process of order one is the fairest competitor.

References

  • Andrada-Félix, J., Fernández-Rodríguez, F.: Improving moving average trading rules with boosting and statistical learning methods. J. Forecast. 27(5), 433–449 (2008)

    Article  Google Scholar 

  • Bai, J., Ng, S.: Boosting diffusion indices. J. Appl. Econ. 24(4), 607–629 (2009)

    Article  Google Scholar 

  • Brautzsch, H.U., Ludwig, U.: Vierteljährliche Entstehungsrechnung des Bruttoinlandsprodukts für Ostdeutschland: Sektorale Bruttowertschöpfung. IWH-Diskussionspapier No. 164 (2002)

  • Buchen, T., Wohlrabe, K.: Forecasting with many predictors: is boosting a viable alternative? Econ. Lett. 113(1), 16–18 (2011)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Bühlmann, P.: Boosting for high-dimensional linear models. Ann. Stat. 34(2), 559–583 (2006)

    Article  Google Scholar 

  • Bühlmann, P., Yu, B.: Boosting with the \(L_2\) loss: regression and classification. J. Am. Stat. Assoc. 98(462), 324–339 (2003)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Ann. Stat. 32(2), 407–499 (2004)

    Article  Google Scholar 

  • Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)

    Article  Google Scholar 

  • 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 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Lehmann, R., Wohlrabe, K.: Regional economic forecasting: state-of-the-art methodology and future challenges. Econ. Bus. Lett. 3(4), 218–231 (2014b)

    Article  Google Scholar 

  • Lehmann, R., Wohlrabe, K.: Forecasting GDP at the regional level with many predictors. Ger. Econ. Rev. 16(2), 226–254 (2015)

    Article  Google Scholar 

  • Lehmann, R., Wohlrabe, K.: Looking into the black box of boosting: the case of Germany. Appl. Econ. Lett. 23(17), 1229–1233 (2016)

    Article  Google Scholar 

  • Nierhaus, W.: Vierteljährliche Volkswirtschaftliche Gesamtrechnungen für Sachsen mit Hilfe temporaler Disaggregation. ifo Dresden berichtet 14(4), 24–36 (2007)

    Google Scholar 

  • Pierdzioch, C., Risse, M., Rohloff, S.: Forecasting gold-price fluctuations: a real-time boosting approach. Appl. Econ. Lett. 22(1), 46–50 (2015)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Ragnitz, J.: Fifteen years after: East Germany revisited. CESifo Forum 6(4), 3–6 (2005)

    Google Scholar 

  • Ragnitz, J.: East Germany today: successes and failures. CESifo DICE Rep. 7(4), 51–58 (2009)

    Google Scholar 

  • Robinzonov, N., Tutz, G., Hothorn, T.: Boosting techniques for nonlinear time series models. AStA Adv. Stat. Anal. 96(1), 99–122 (2012)

    Article  Google Scholar 

  • Vullhorst, U.: Zur indikatorgestützten Berechnung des vierteljährlichen Bruttoinlandsprodukts für Baden-Württemberg. Statistisches Monatsheft Baden-Württemberg 6, 32–35 (2008)

    Google Scholar 

  • Zeng, J.: Forecasting aggregates with disaggregate variables: does boosting help to select the most relevant predictors? J. Forecast. (2016, forthcoming)

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Robert Lehmann.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lehmann, R., Wohlrabe, K. Boosting and regional economic forecasting: the case of Germany. Lett Spat Resour Sci 10, 161–175 (2017). https://doi.org/10.1007/s12076-016-0179-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12076-016-0179-1

Keywords

JEL Classification

Navigation