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The Joint Treatment of Exact and Stochastic Restrictions
Author(s) -
Shonkwiler J. S.
Publication year - 1982
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1982.tb00830.x
Subject(s) - estimator , ordinary least squares , mathematics , minimum variance unbiased estimator , variance (accounting) , generalized least squares , least squares function approximation , bias of an estimator , consistent estimator , statistics , mathematical optimization , accounting , business
Summary In regression analysis both exact and stochastic extraneous information may be represented via restrictions on the parameters of a linear model which then may be estimated by applying constrained generalized least squares. It is shown that this estimator can be recast as a computationally simpler estimator that is a combination of the ordinary least squares estimator and the discrepancy between the OLS estimator and both types of restrictions. The variance of the restricted parameters is explicitly shown to depend on the variance of the extraneous information.