Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany

25 Pages Posted: 14 Mar 2013

See all articles by Teresa Buchen

Teresa Buchen

CESifo (Center for Economic Studies and Ifo Institute) - Ifo Institute

Klaus Wohlrabe

CESifo (Center for Economic Studies and Ifo Institute) - Ifo Institute

Date Written: March 13, 2013

Abstract

The use of large datasets for macroeconomic forecasting has received a great deal of interest recently. Boosting is one possible method of using high-dimensional data for this purpose. It is a stage-wise additive modelling procedure, which, in a linear specification, becomes a variable selection device that iteratively adds the predictors with the largest contribution to the fit. Using data for the United States, the euro area and Germany, we assess the performance of boosting when forecasting a wide range of macroeconomic variables. Moreover, we analyse to what extent its forecasting accuracy depends on the method used for determining its key regularisation parameter, the number of iterations. We find that boosting mostly outperforms the autoregressive benchmark, and that K-fold cross-validation works much better as stopping criterion than the commonly used information criteria.

Keywords: macroeconomic forecasting, component-wise boosting, large datasets, variable selection, model selection criteria

JEL Classification: C320, C520, C530, E370

Suggested Citation

Buchen, Teresa and Wohlrabe, Klaus, Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany (March 13, 2013). CESifo Working Paper Series No. 4148. Available at SSRN: https://ssrn.com/abstract=2232696

Teresa Buchen

CESifo (Center for Economic Studies and Ifo Institute) - Ifo Institute ( email )

Poschinger Str. 5
Munich, 01069
Germany

Klaus Wohlrabe (Contact Author)

CESifo (Center for Economic Studies and Ifo Institute) - Ifo Institute ( email )

Poschinger Str. 5
Munich, 01069
Germany

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