Estimation of SUR Model with Non-nested Missing Observations

This paper considers alternative two-step estimators and their small sample properties for the seemingly unrelated regression (SUR) model with non-nested missing observations. A Monte Carlo experiment indicates that alternative estimators have more profound differences in their efficiency, compared to the case of nested missing observations. In particular, the two-step application of the Hartley-Hocking maximum likelihood estimator can realize a significant gain in efficiency. There are substantial losses in efficiency when only the subset of data that has complete observations is used in estimation.