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Comparing Single-Equation Estimators in a Simultaneous Equation System

Published online by Cambridge University Press:  18 October 2010

T. W. Anderson ,
Naoto Kunitomo  and
Kimio Morimune
T. W. Anderson
Affiliation:
Stanford University
Naoto Kunitomo
Affiliation:
University of Tokyo
Kimio Morimune
Affiliation:
Kyoto University
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Abstract

Comparisons of estimators are made on the basis of their mean squared errors and their concentrations of probability computed by means of asymptotic expansions of their distributions when the disturbance variance tends to zero and alternatively when the sample size increases indefinitely. The estimators include k-class estimators (limited information maximum likelihood, two-stage least squares, and ordinary least squares) and linear combinations of them as well as modifications of the limited information maximum likelihood estimator and several Bayes' estimators. Many inequalities between the asymptotic mean squared errors and concentrations of probability are given. Among medianunbiasedestimators, the limited information maximum likelihood estimator dominates the median-unbiased fixed k-class estimator.

Information

Type
Research Article
Information
Econometric Theory , Volume 2 , Issue 1 , April 1986 , pp. 1 - 32
Copyright
Copyright © Cambridge University Press 1986 

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