An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
作者:
Arnold Zellner,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1962)
卷期:
Volume 57,
issue 298
页码: 348-368
ISSN:0162-1459
年代: 1962
DOI:10.1080/01621459.1962.10480664
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
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 [1] 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.
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