Nonparametric Seemingly Unrelated Regression

This paper presnets a method for simultaneously estimating a system of nonparametric multiple regressions which may seem unrelated, but where the errors are potentially correlated between equations. We show that the prime advantage of estimating such a 'seemingly unrelated' system of nonparametric regressions is that substantially less observations can be required to obtain reliable functions estimates than if each of the regression equations was estimated separately and the correlation ignored.