Factor augmented bridge models (FABM) and soft indicators to forecast italian industrial production

This paper presents a new forecasting approach straddling the conventional methods applied to the Italian industrial production index. Specifically, the proposedmethod treats factormodels and bridge models as complementary ingredients feeding a unique model specification. We document that the proposed approach improves upon traditional bridge models bymaking efficient use of the information conveyed by a large amount of survey data onmanufacturing activity. Different factor algorithms are compared and, under the provision that a large estimation window is used, partial least squares outperform principal component-based alternatives