Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/70933
Authors: 
Buchen, Teresa
Wohlrabe, Klaus
Year of Publication: 
2013
Series/Report no.: 
CESifo Working Paper: Fiscal Policy, Macroeconomics and Growth 4148
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.
Subjects: 
macroeconomic forecasting
component-wise boosting
large datasets
variable selection
model selection criteria
JEL: 
C32
C52
C53
E37
Document Type: 
Working Paper

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