Variable Selection in Seemingly Unrelated Regressions with Random Predictors

Abstract

This paper considers linear model selection when the response is vector-valued and the predictors, either all or some, are randomly observed. We propose a new approach that decouples statistical inference from the selection step in a “post-inference model summarization” strategy. We study the impact of predictor uncertainty on the model selection procedure. The method is demonstrated through an application to asset pricing.