In this paper, we investigate seemingly unrelated regression (SUR) models that allow the number of equations (N) to be large, and comparable observations in each equation (T). It is well known literature conventional SUR estimator, for example, generalized least squares (GLS) estimator Zellner (1962) does not perform well. As main contribution propose a new feasible GLS called graphical lasso (...

We present a sparse seemingly unrelated regression (SSUR) model to generate substantively relevant structures in the high-dimensional distributions of seemingly unrelated model (SUR) parameters. This SSUR framework includes prior specifications, posterior computations using Markov chain Monte Carlo methods, evaluations of model uncertainty, and model structure searches. Extensions of the SSUR m...