Factor construction methods are widely used to summarize a large panel of variables by means of a relatively small number of representative factors. We propose a novel factor construction procedure that enjoys the properties of robustness to outliers and of sparsity; that is, having relatively few nonzero factor loadings. Compared to more traditional factor construction methods, we find that this procedure leads to better interpretable factors and to a favorable forecasting performance, both in a Monte Carlo experiment and in two empirical applications to large data sets, one from macroeconomics and one from microeconomics.
# 11-122/4 (2011-08-19)
- Christophe Croux, K.U. Leuven, Belgium; Peter Exterkate, Erasmus University Rotterdam
- dimension reduction, forecasting, outliers, regularization
- JEL codes:
- C38, C51, C53