References
- Bańbura, M., and G. Rünstler. 2011. “A Look into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP.” International Journal of Forecasting 27 (2): 333–346. doi:10.1016/j.ijforecast.2010.01.011.
- Buchen, T., and K. Wohlrabe. 2011. “Forecasting with Many Predictors: Is Boosting a Viable Alternative?” Economics Letters 113 (1): 16–18. doi:10.1016/j.econlet.2011.05.040.
- Buchen, T., and K. Wohlrabe. 2014. “Assessing the Macroeconomic Forecasting Performance of Boosting – Evidence for the United States, the Euro Area, and Germany.” Journal of Forecasting 33 (4): 231–242. doi:10.1002/for.2293.
- Bühlmann, P., and B. Yu. 2003. “Boosting with the L2 Loss: Regression and Classification.” Journal of the American Statistical Association 98 (462): 324–339. doi:10.1198/016214503000125.
- Friedman, J. H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics 29 (5): 1189–1232. doi:10.1214/aos/1013203451.
- Kim, H. H., and N. R. Swanson. 2014. “Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods: New Empirical Evidence.” Journal of Econometrics 178 (2): 352–367. doi:10.1016/j.jeconom.2013.08.033.
- Pierdzioch, C., M. Risse, and S. Rohloff. 2015. “Forecasting Gold-Price Fluctuations: A Real-Time Boosting Approach.” Applied Economics Letters 22 (1): 46–50. doi:10.1080/13504851.2014.925040.
- Pierdzioch, C., M. Risse, and S. Rohloff. 2016. “A Boosting Approach to Forecasting Gold and Silver Returns: Economic and Statistical Forecast Evaluation.” Applied Economics Letters 23 (5): 347–352. doi:10.1080/13504851.2015.1073835.