Median regression for SUR models with the same explanatory variables in each equation

In this paper we introduce an interesting feature of the generalized least absolute deviations method for seemingly unrelated regression equations (SURE) models. Contrary to the collapse of generalized leastsquares parameter estimations of SURE models to the ordinary least-squares estimations of the individual equations when the same regressors are common between all equations, the estimations of the proposed methodology are not identical to the least absolute deviations estimations of the individual equations. This is important since contrary to the least-squares methods, one can take advantage of efficiency gain due to cross-equation correlations even if the system includes the same regressors in each equation.