MULTIVARIATE REGRESSION-MODELS FOR DISCRETE AND CONTINUOUS REPEATED MEASUREMENTS

A general class of multivariate regression models is considered for repeated measurements with discrete and continuous outcome variables. The proposed model is based on the seemingly unrelated regression model (Zellner, 1962) and an ''tension of the model of Park and Woolson (1992). The regression parameters of the model axe consistently estimated using the two-stage least squares method. When the outcome variables are multivariate normal, the two-stage estimator reduces to Zellner's two-stage estimator. As a special case, we consider the marginal distribution described by Liang and Zeger (1986). Under this distributional assumption, we show that the two-stage estimator has similar asymptotic properties and comparable small sample properties to Liang and Zeger's estimator. Since the proposed approach is based on the least squares method, however, any distributional assumption is not required for outcome variables. As a results, the proposed estimator is more robust to the marginal distribution of outcomes.