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Comparison of k-Class Estimators When the Disturbances Are Small
Joseph B. Kadane
Vol. 39, No. 5 (Sep., 1971), pp. 723-737
Published by: The Econometric Society
Stable URL: http://www.jstor.org/stable/1909575
Page Count: 15
You can always find the topics here!Topics: Estimators, Least squares, Statistical estimation, Estimators for the mean, Maximum likelihood estimation, Econometrics, Analytical estimating, Economic research, Matrices, Economic systems
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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.
Econometrica © 1971 The Econometric Society