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Forecasting with many predictors - Is boosting a viable alternative?

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  • Buchen, Teresa
  • Wohlrabe, Klaus

Abstract

This paper evaluates the forecast performance of boosting, a variable selection device, and compares it with the forecast combination schemes and dynamic factor models presented in Stock and Watson (2006). Using the same data set and comparison methodology, we find that boosting is a serious competitor for forecasting US industrial production growth in the short run and that it performs best in the longer run.

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File URL: http://epub.ub.uni-muenchen.de/11788/1/masterfile_boosting.pdf
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Bibliographic Info

Paper provided by University of Munich, Department of Economics in its series Discussion Papers in Economics with number 11788.

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Date of creation: 06 Sep 2010
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Handle: RePEc:lmu:muenec:11788

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Keywords: Forecasting; Boosting; Cross-validation;

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  1. Shafik, Nivien & Tutz, Gerhard, 2009. "Boosting nonlinear additive autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2453-2464, May.
  2. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
  3. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
  4. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
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Cited by:
  1. Souhaib Ben Taieb & Rob J Hyndman, 2014. "Boosting multi-step autoregressive forecasts," Monash Econometrics and Business Statistics Working Papers 13/14, Monash University, Department of Econometrics and Business Statistics.
  2. 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.

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