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 …

The forecasting of government revenues is extremely important for an adequate budget execution, since a good accuracy …

The forecasting of government revenues is extremely important for an adequate budget execution, since a good accuracy …

provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly … precise forecasts than the other and than alternative regularization strategies, such as principal component regression …

provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly … precise forecasts than the other and than alternative regularization strategies, such as principal component regression …

The crisis periods of the past decades have highlighted the difficulty of forecasting economic indicators due to …-dimensional dataset. Our approach is straightforward and can be seamlessly incorporated into existing forecasting frameworks. We … dataset, we show that the TSF framework substantially improves the forecasting accuracy across all models, with notable gains …

The crisis periods of the past decades have highlighted the difficulty of forecasting economic indicators due to …-dimensional dataset. Our approach is straightforward and can be seamlessly incorporated into existing forecasting frameworks. We … dataset, we show that the TSF framework substantially improves the forecasting accuracy across all models, with notable gains …