Shapley Value Regression and the Resolution of Multicollinearity

Sudhanshu Mishra ()

Journal of Economics Bibliography, 2016, vol. 3, issue 3, 498-515

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 develops a Fortran computer program that executes it. It also retrieve regression coefficients from the Shapley value. 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.

Keywords: Multicollinearity; Shapley value; regression; computer program; Fortran (search for similar items in EconPapers)
JEL-codes: C63 C71 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (11)

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Working Paper: Shapley value regression and the resolution of multicollinearity (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:ksp:journ6:v:3:y:2016:i:3:p:498-515

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