Statistical remedies against macro information overload

“Dimension reduction” condenses the information content of a multitude of data series into small manageable set of factors or functions. This reduction is important for forecasting with macro variables because¬†many data series have only limited and highly correlated information content. There are three types of statistical methods.The first type selects a subset of “best” explanatory variables (view post here). The second type selects a small set of latent background factors of all explanatory variables and then uses these background factors for prediction (Dynamic Factor Models). The third type generates a small set of functions of the original explanatory variables that historically would have retained their explanatory power and then deploys these for forecasting (Sufficient Dimension Reduction).