Sparse and Robust Factor Modelling - Christophe Croux; Peter Exterkate (Tinbergen Institute, 2011)
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.
urn:NBN:nl:ui:15-1765/25712
Keywords: C38; C51; C53; 330; dimension reduction; forecasting; outliers; regularization