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Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany

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  • Teresa Buchen
  • Klaus Wohlrabe

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Abstract

The use of large datasets for macroeconomic forecasting has received a great deal of interest recently. Boosting is one possible method of using high-dimensional data for this purpose. It is a stage-wise additive modelling procedure, which, in a linear specification, becomes a variable selection device that iteratively adds the predictors with the largest contribution to the fit. Using data for the United States, the euro area and Germany, we assess the performance of boosting when forecasting a wide range of macroeconomic variables. Moreover, we analyse to what extent its forecasting accuracy depends on the method used for determining its key regularisation parameter, the number of iterations. We find that boosting mostly outperforms the autoregressive benchmark, and that K-fold cross-validation works much better as stopping criterion than the commonly used information criteria.

Suggested Citation

  • Teresa Buchen & Klaus Wohlrabe, 2013. "Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany," CESifo Working Paper Series 4148, CESifo Group Munich.
  • Handle: RePEc:ces:ceswps:_4148
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    Citations

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    Cited by:

    1. Robert Lehmann & Klaus Wohlrabe, 2017. "Boosting and regional economic forecasting: the case of Germany," Letters in Spatial and Resource Sciences, Springer, vol. 10(2), pages 161-175, July.
    2. 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.
    3. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, pages 745-759.
    4. Götz, Thomas B. & Knetsch, Thomas A., 2017. "Google data in bridge equation models for German GDP," Discussion Papers 18/2017, Deutsche Bundesbank.
    5. 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.
    6. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A quantile-boosting approach to forecasting gold returns," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 38-55.
    7. Barrow, Devon K. & Crone, Sven F., 2016. "A comparison of AdaBoost algorithms for time series forecast combination," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1103-1119.
    8. Jing Zeng, 2014. "Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?," Working Paper Series of the Department of Economics, University of Konstanz 2014-20, Department of Economics, University of Konstanz.
    9. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions in Germany With Boosted Regression Trees," Macroeconomics and Finance Series 201505, Hamburg University, Department Wirtschaft und Politik.
    10. Christian Pierdzioch & Rangan Gupta, 2017. "Uncertainty and Forecasts of U.S. Recessions," Working Papers 201732, University of Pretoria, Department of Economics.
    11. Matthias Vogl & Reiner Leidl, 2016. "Informing management on the future structure of hospital care: an extrapolation of trends in demand and costs in lung diseases," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(4), pages 505-517, May.

    More about this item

    Keywords

    macroeconomic forecasting; component-wise boosting; large datasets; variable selection; model selection criteria;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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