Forecasting with many predictors - Is boosting a viable alternative?
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.
|Date of creation:||06 Sep 2010|
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- Shafik, Nivien & Tutz, Gerhard, 2009. "Boosting nonlinear additive autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2453-2464, May.
- CapistrÃ¡n, Carlos & Timmermann, Allan, 2009.
"Forecast Combination With Entry and Exit of Experts,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 27(4), pages 428-440.
- Carlos Capistrán & Allan Timmermann, 2006. "Forecast Combination with Entry and Exit of Experts," Working Papers 2006-08, Banco de México.
- Carlos Capistrán & Allan Timmermann, 2008. "Forecast Combination With Entry and Exit of Experts," CREATES Research Papers 2008-55, Department of Economics and Business Economics, Aarhus University.
- Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
- Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
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