This note develops a slightly different formulation of one of the basic results presented in a recent paper by Wallace and Hussain [5] on error components models for disturbances in relationships designed to explain cross-sectional observations over time. In their discussion, Wallace and Hussain derive the inverse of the variance-covariance matrix of the disturbances by trial and error. Unfortunately, their formulation does lead to a "natural" interpretation of the generalized least squares estimates, or of the relationships of these estimates to other estimates in the same way diagonalization of the variance-covariance matrix by means of an appropriate orthogonal transformation does. The characteristic roots of the variance-covariance matrix for the disturbances in a three component model which has been studied by Wallace and Hussain are derived here. It is shown how knowledge of these roots and the characteristic vectors associated with them leads to a form of the inverse matrix which may be more readily interpreted, as well as a number of other useful results, including an interpretation of the poor small sample properties of estimates which incorporate dummy variables for each individual.