An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
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DOI：
10.1080/01621459.1962.10480664
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发表时间：
1962-06
影响因子：
3.7
通讯作者：
A. Zellner
中科院分区：
文献类型：
--
作者：
A. Zellner
摘要本文报道了一种估计回归方程组参数的方法，该方法将艾特肯广义最小二乘[1]应用于整个方程组。在实际中通常遇到的条件下，我们发现，这样得到的回归系数估计量至少比逐个方程应用最小二乘法得到的回归系数估计量渐近地更有效。如果不同方程中的“独立”变量不高度相关，并且不同方程中的干扰项高度相关，则效率的增益可能相当大。此外，还描述了基于“微观”和“宏观”数据的所有回归方程系数向量相等假设的检验。如果这个假设被接受，就不会有聚集偏差。最后，对两家公司1935-1954年的年度投资数据进行了估计程序和聚集偏差的“微观检验”分析。
Abstract 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.