The reduced-rank regression is an effective method to predict multiple response variables from the same set of predictor variables, because it can reduce the number of model parameters as well as take advantage of interrelations between the response variables and therefore improve predictive accuracy. We propose to add a new feature to the reduced-rank regression that allows selection of releva...

The authors consider dimensionality reduction methods used for prediction, such as reduced rank regression, principal component regression and partial least squares. They show how it is possible to obtain intermediate solutions by estimating simultaneously the latent variables for the predictors and for the responses. They obtain a continuum of solutions that goes from reduced rank regression t...

This paper proposes a reduced rank regression framework for constructing coincident and leading indexes. Based on a formal definition that requires that the first differences of the leading index are the best linear predictor of the first differences of the coincident index, it is shown that the notion of polynomial serial correlation common features can be used to build these composite variabl...