A New Biased Estimator to Combat the Multicollinearity of the Gaussian Linear Regression Model

Issam Dawoud and B. M. Golam Kibria
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Issam Dawoud: Department of Mathematics, Al-Aqsa University, Gaza 4051, Palestine
B. M. Golam Kibria: Department of Mathematics and Statistics, Florida International University, Miami, FL 33199, USA

Stats, 2020, vol. 3, issue 4, 1-16

Abstract: In a multiple linear regression model, the ordinary least squares estimator is inefficient when the multicollinearity problem exists. Many authors have proposed different estimators to overcome the multicollinearity problem for linear regression models. This paper introduces a new regression estimator, called the Dawoud–Kibria estimator, as an alternative to the ordinary least squares estimator. Theory and simulation results show that this estimator performs better than other regression estimators under some conditions, according to the mean squares error criterion. The real-life datasets are used to illustrate the findings of the paper.

Keywords: Dawoud–Kibria estimator; Liu estimator; Monte Carlo simulation; multicollinearity; MSE; ridge regression estimator (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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