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CiteWeb id: 20120000029

CiteWeb score: 4914

In this paper a method of estimating the parameters of a set of regression equations is reported which involves application of Aitken's generalized least-squares [11 to the whole system of equations. Under conditions generally encountered in practice, it is found that the regression coefficient estimators so obtained are at least asymptotically more efficient than those obtained by an equation-by-equation application of least squares. This gain in efficiency can be quite large if "independent" variables in different equations are not highly correlated and if disturbance terms in different equations are highly correlated. Further, tests of the hypothesis that all regression equation coefficient vectors are equal, based on "micro" and "macro" data, are described. If this hypothesis is accepted, there will be no aggregation bias. Finally, the estimation procedure and the "micro-test" for aggregation bias are applied in the analysis of annual investment data, 1935-1954, for two firms.

The publication "An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias" is placed in the Top 10000 of the best publications in CiteWeb. Also in the category Mathematics it is included to the Top 1000. Additionally, the publicaiton "An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias" is placed in the Top 100 among other scientific works published in 2012.
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