varying variances in regression models. A small Monte Carlo study indicates that the method works reasonably well for …

This paper revisits a number of data-rich prediction methods, like factor models, Bayesian ridge regression and … method: partial least squares regression. Under the latter, linear, orthogonal combinations of a large number of predictor …. We also argue that forecast combinations can be interpreted as a restricted form of partial least squares regression …

This paper considers the problem of statistical inference in linear regression models whose stochastic regressors and … regression hypotheses. …

The question of variable selection in a regression model is a major open research topic in econometrics. Traditionally …

known alternative: partial least squares (PLS) regression. In this method, linear, orthogonal combinations of a large number … maximized. We show theoretically that when the data have a factor structure, PLS regression can be seen as an alternative way to … possibly vanishes in the limit, PLS regression still provides asymptotically the best fit for the target variable of interest …