Mishra, SK (2016): Shapley value regression and the resolution of multicollinearity.

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Abstract
Multicollinearity in empirical data violates the assumption of independence among the regressors in a linear regression model that often leads to failure in rejecting a false null hypothesis. It also may assign wrong sign to coefficients. Shapley value regression is perhaps the best methods to combat this problem. The present paper simplifies the algorithm of Shapley value decomposition of R2 and provides a computer program that executes it. However, Shapley value regression becomes increasingly impracticable as the number of regressor variables exceeds 10, although, in practice, a good regression model may not have more than ten regressors.
Item Type:  MPRA Paper 

Original Title:  Shapley value regression and the resolution of multicollinearity 
Language:  English 
Keywords:  Multicollinearity, Shapley value, regression, computational algorithm, computer program, Fortran 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling C  Mathematical and Quantitative Methods > C7  Game Theory and Bargaining Theory > C71  Cooperative Games 
Item ID:  72116 
Depositing User:  Sudhanshu Kumar Mishra 
Date Deposited:  20 Jun 2016 14:09 
Last Modified:  20 Jun 2016 14:10 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/72116 