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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

    ()

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.

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Paper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 4148.

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Date of creation: 2013
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Handle: RePEc:ces:ceswps:_4148

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

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  1. Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," Working Papers 284, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    • Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," NBER Chapters, in: NBER Macroeconomics Annual 2004, Volume 19, pages 161-224 National Bureau of Economic Research, Inc.
  2. Buchen, Teresa & Wohlrabe, Klaus, 2010. "Forecasting with many predictors - Is boosting a viable alternative?," Discussion Papers in Economics, University of Munich, Department of Economics 11788, University of Munich, Department of Economics.
  3. Francesco Audrino & Fabio Trojani, 2007. "Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent," University of St. Gallen Department of Economics working paper series 2007, Department of Economics, University of St. Gallen 2007-24, Department of Economics, University of St. Gallen.
  4. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," National Institute Economic Review, National Institute of Economic and Social Research, National Institute of Economic and Social Research, vol. 203(1), pages 109-115, January.
  5. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, 08.
  6. Julián Andrada-Félix & Fernando Fernández-Rodr�guez, 2008. "Improving moving average trading rules with boosting and statistical learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 27(5), pages 433-449.
  7. Katja Drechsel & R. Scheufele, 2013. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," IWH Discussion Papers, Halle Institute for Economic Research 7, Halle Institute for Economic Research.
  8. Audrino, Francesco & Barone-Adesi, Giovanni, 2005. "Functional gradient descent for financial time series with an application to the measurement of market risk," Journal of Banking & Finance, Elsevier, Elsevier, vol. 29(4), pages 959-977, April.
  9. Shafik, Nivien & Tutz, Gerhard, 2009. "Boosting nonlinear additive autoregressive time series," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 53(7), pages 2453-2464, May.
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