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Boosting und die Prognose der deutschen Industrieproduktion: Was verrät uns der Blick in die Details?

Author

Listed:
  • Robert Lehmann
  • Klaus Wohlrabe

Abstract

Der Artikel zeigt auf, dass Boosting, eine neuere Methode, große Datensätze für die ökonomische Prognose zu nutzen, einen wesentlichen Beitrag zur Verbesserung der Prognose der Industrieproduktion liefern kann. Konkret geht er der Frage nach, welche Indikatoren vom Boosting-Algorithmus zur Vorhersage der deutschen Industrieproduktion im Zeitraum 1996 bis 2014 ausgewählt werden. Im Ergebnis zeigt sich, dass sowohl harte Indikatoren, wie Auftragseingänge oder Umsätze, als auch Befragungsindikatoren regelmäßig in das Prognosemodell aufgenommen werden.

Suggested Citation

  • Robert Lehmann & Klaus Wohlrabe, 2016. "Boosting und die Prognose der deutschen Industrieproduktion: Was verrät uns der Blick in die Details?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 69(03), pages 30-33, February.
    • Handle: RePEc:ces:ifosdt:v:69:y:2016:i:03:p:30-33
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    References listed on IDEAS

    as
    1. 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.
    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. Berg Tim Oliver, 2017. "Forecast accuracy of a BVAR under alternative specifications of the zero lower bound," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(2), pages 1-29, April.
    4. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    5. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    6. Tim Oliver Berg, 2016. "Multivariate Forecasting with BVARs and DSGE Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(8), pages 718-740, December.
    7. 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.
    8. Buchen, Teresa & Wohlrabe, Klaus, 2011. "Forecasting with many predictors: Is boosting a viable alternative?," Economics Letters, Elsevier, vol. 113(1), pages 16-18, October.
    9. 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.
    10. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Industrieproduktion; Algorithmus; Prognose; Deutschland;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General

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