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Cited by 8,401

The DOI matches its Crossref record.

An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias

Zellner, Arnold·1962·Journal of the American Statistical Association
DOI 10.1080/01621459.1962.10480664
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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.

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Metadata

Authors
Zellner, A.
Venue
Journal of the American Statistical Association
Publisher
Informa UK Limited
Published
1962-06
Volume
57
Issue
298
Pages
348-368
ISSN
  • 0162-1459 (ISSN-L)
  • 1537-274X
DOI
10.1080/01621459.1962.10480664
OpenAlex
W2094514178
Cited by
8,401
References
0
FWCI
8.86
Type
journal article
Language
en
Open access
closed

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