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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 in comparison to the forecast combination schemes and dynamic factor models presented in Stock and Watson (2006). We find that boosting is a serious competitor for forecasting US industrial production.

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Bibliographic Info

Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 113 (2011)
Issue (Month): 1 (October)
Pages: 16-18

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Handle: RePEc:eee:ecolet:v:113:y:2011:i:1:p:16-18

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Web page: http://www.elsevier.com/locate/ecolet

Related research

Keywords: Forecasting Boosting Large datasets;

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  1. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
  2. Timmermann, Allan G, 2005. "Forecast Combinations," CEPR Discussion Papers 5361, C.E.P.R. Discussion Papers.
  3. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
  4. Shafik, Nivien & Tutz, Gerhard, 2009. "Boosting nonlinear additive autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2453-2464, May.
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Cited by:
  1. 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.
  2. 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.

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