The quasi-stochastically constrained least squares method for ill linear regression

Siming Li, Yao Sheng and Yong Li

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 2, 217-225

Abstract: This article concerns the stochastically constrained linear model under a biased assumption. We propose a quasi-stochastically constrained least squares estimator. Furthermore, we provide the expectation of this estimator, demonstrate its consistency and asymptotic normality. In the end of the article, the simulation study of the new estimator shows that it is superior to the least squares estimator, ridge estimator, and the linear constrained estimators under certain conditions by comparing the mean squared errors of these estimators.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:45:y:2016:i:2:p:217-225

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DOI: 10.1080/03610926.2013.828076

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