In this thesis we look at partial least squares (PLS), a technique which constructs a
scheme for extracting orthogonal unobserved components based on the covariance between
the predictors and the forecasting variable. Nonetheless, PLS methods are based on all
variables, that is, it gives weight to all the predictors in the dataset; and then, the weight given to predictors with high predictive power is weakened. Therefore, we introduce into the
economic analysis the sparse partial least squares (SPLS) approach, proposed by Chun and
Keles (2010) in the context of chemometrics. In order to obtain a sparse solution, that is, to
construct the factors based on a reduced number of predictors, this method includes an L1
penalty in the PLS formulation. This type of regularization allows selecting the predictors that
contain relevant information and discarding those that have redundant information or
negligible effect on the forecasting target variable. We extend the static implementation to a
dynamic one, with the aim of considering the macroeconomic time series properties.Programa Oficial de Doctorado en Economía de la Empresa y Métodos CuantitativosPresidente: Antoni Espasa Terrades; Secretario: Arielle Beyaert Stevens; Vocal: Máximo Camacho Alons
