Looking into the Black Box of Boosting: The Case of Germany

Lehmann, Robert and Wohlrabe, Klaus (2015): Looking into the Black Box of Boosting: The Case of Germany.

Abstract

This paper looks into the 'fine print' of boosting for economic forecasting. By using German industrial production for the period from 1996 to 2014 and a data set consisting of 175 monthly indicators, we evaluate which indicators get selected by the boosting algorithm over time and four different forecasting horizons. It turns out that a number of hard indicators like turnovers, as well as a small number of survey results, get selected frequently by the algorithm and are therefore important to forecasting the performance of the German economy. However, there are indicators such as money supply that never get chosen by the boosting approach at all.

• Item Type: MPRA Paper
• Language: English
• Keywords: boosting; economic forecasting; industrial production
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

References

Banbura, M. and Rünstler, G. (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.

Buchen, T. and Wohlrabe, K. (2011). Forecasting with many predictors: Is boosting a viable alternative? Economics Letters, 113 (1), 16–18.

Buchen, T. and Wohlrabe, K. (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.

Bühlmann, P. and Yu, B. (2003). Boosting with the L2 loss: Regression and Classification. Journal of the American Statistical Association, 98 (462), 324–339.

Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29 (5), 1189–1232.

Kim, H. H. and Swanson, N. R. (2014). Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence. Journal of Econometrics, 178 (2), 352–367.

Pierdzioch, C., Risse, M. and Rohloff, S. (2015a). A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation. Applied Economics Letters, forthcoming.

Pierdzioch, C., Risse, M. and Rohloff, S. (2015b). Forecasting gold-price fluctuations: a real-time boosting approach. Applied Economics Letters, 22 (1), 46–50.