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Journal Article

Comparison of k-Class Estimators When the Disturbances Are Small

Joseph B. Kadane
Econometrica
Vol. 39, No. 5 (Sep., 1971), pp. 723-737
Published by: The Econometric Society
DOI: 10.2307/1909575
Stable URL: http://www.jstor.org/stable/1909575
Page Count: 15
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Comparison of k-Class Estimators When the Disturbances Are Small
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Abstract

A new approach to the choice of econometric estimators, called small-sigma asymptotics, is introduced and applied to the choice of k-class estimators of the parameters of a single equation in a system of linear simultaneous stochastic equations. I find that when the degree of overidentification is no more than six, the two stage least squares estimator uniformly dominates the limited information maximum likelihood estimator in a certain sense. The small sigma method can be used on many problems in statistics and econometrics.

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