Computes a GLS matrix weighted estimator for a panel data set. meangroup.src does a similar estimator, but uses simple weighted average rather than a matrix-weighted average. Swamy(1970), Efficient Inference in a Random Coefficient Regression Model, Econometrica, vol 38, 311-323. (This abstract was borrowed from another version of this item.)

[Using a general definition of a generalized inverse of a singular matrix we generalized the k class and three stage least squares procedures so that they can be applied when the sample size, say T, is smaller than the number of exogenous variables, say K, in a system of equations. These generalized k class and three stage least squares estimators, in usual cases, coincide with ordinary least squares and Zellner's [7] efficient estimators respectively as long as T @ extless K and coincide with the usual k class and three stage least squares estimators respectively as T exceeds K.]

Wallace and Hussain (1969) considered the use of an error components regression model in the analysis of time series of cross-sections and developed an estimator of the coefficient vector based on an estimated variance-covariance matrix of error terms. In this paper, we have shown that under the set of assumptions adopted by Wallace and Hussain there are an infinite number of estimators which have the same asymptotic variancecovariance matrix as the Wallace-Hussain estimator and also that it is not possible to choose an estimator on the basis of asymptotic efficiency. We have developed an alternative estimator of the variance-covariance matrix of error terms and have used this estimator in developing a feasible Aitken type estimator for the coefficient vector. We have derived some small sample properties of this estimator and have compared them with those of other estimators of the coefficient vector.