Abstract
When the panel is incomplete, which is the rule rather than the exception, standard estimation methods cannot be applied. This paper considers a model with spatial lag and two way-way error components regression with unbalanced data. The paper derives several estimators for structural parameters. It also develops more intensively ANOVA estimators for covariance components. The Monte Carlo experiments in which the design varies in, (a) the degree of unbalancedness in the data, (b) the variance components ratio, (c) the spatial matrix and (d) the spatial coefficient, compare the performance of theses estimators. Some of the basic findings are the following: (1) Better estimates of the variance components do not necessarily imply better estimates of the regression coefficients. (2) Making the data balanced, by dropping observations, worsens the performance of these estimators when compared to those from the entire unbalanced data.
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