A Novel Approach for Estimating Seemingly Unrelated Regressions with High-Order Autoregressive Disturbances

Abstract

A seemingly unrelated regression (SUR) model is defined by a system of linear regression equations in which the disturbances are contemporaneously correlated across equations. However, the disturbances can also be serially correlated in each equation of the system. In these cases, estimating SUR becomes more complicated. Some methods have been considered estimating SUR with low-order autoregressive (AR) disturbances. In this article, SUR with high-order AR disturbances are considered and a tapering approach is examined under this situation. Two modified methods for estimating SUR are obtained by using this approach. A comprehensive Monte Carlo simulation study is performed in order to compare small-sample efficiencies of the modified methods with the others given in the literature.