Operational variants of the minimum mean squared error estimator in linear regression models with non-spherical disturbances

There is a good deal of literature that investigates the properties of various operational variants of Theil's (1971, Principles of Econometrics, Wiley, New York) minimum mean squared error estimator. It is interesting that virtually all of the existing analysis to date is based on the premise...

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Bibliographic Details
Published in:Annals of the Institute of Statistical Mathematics 2000-06, Vol.52 (2), p.332-342
Main Authors: WAN, A. T. K, CHATURVEDI, A
Format: Article
Language:English
Subjects:
Applications
Estimating techniques
Exact sciences and technology
Insurance, economics, finance
Linear inference, regression
Mathematical models
Mathematics
Monte Carlo simulation
Probability and statistics
Regression analysis
Sciences and techniques of general use
Statistics
Studies
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Summary:There is a good deal of literature that investigates the properties of various operational variants of Theil's (1971, Principles of Econometrics, Wiley, New York) minimum mean squared error estimator. It is interesting that virtually all of the existing analysis to date is based on the premise that the model's disturbances are i.i.d., an assumption which is not satisfied in many practical situations. In this paper, we consider a model with non-spherical errors and derive the asymptotic distribution, bias and mean squared error of a general class of feasible minimum mean squared error estimators. A Monte-Carlo experiment is conducted to examine the performance of this class of estimators in finite samples. [PUBLICATION ABSTRACT]
ISSN:0020-3157
1572-9052
DOI:10.1023/A:1004169923370