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 propose an efficient semiparametric estimator for the coefficients of a multivariate linear regression model — with a conditional quantile restriction for each equation — in which the conditional distributions of errors given regressors are unknown. The procedure can be used to estimate multiple conditional quantiles of the same regression relationship. The proposed estimator is asymptotical...