Shapley value regression and the resolution of multicollinearity

Sudhanshu Mishra ()

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Multicollinearity; Shapley value; regression; computational algorithm; computer program; Fortran (search for similar items in EconPapers)
JEL-codes: C13 C4 C63 C71 (search for similar items in EconPapers)
Date: 2016-06-17
New Economics Papers: this item is included in nep-gth and nep-net
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Related works:
Journal Article: Shapley Value Regression and the Resolution of Multicollinearity (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:72116

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