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Shapley Value Regression and the Resolution of Multicollinearity

Journal of Economics Bibliography

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Field Value
 
Title Shapley Value Regression and the Resolution of Multicollinearity
 
Creator MISHRA, Sudhanshu K.; Avantika, Rohini Sector-1
Delhi – 110085

Retd. Professor, Dept. of economics

North-Eastern Hill University, Shillong (India): 91 - 793022
 
Subject Multicollinearity; Shapley value; Regression; Computer program; Fortran.
C63; C71.
 
Description 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.JEL. C63, C71.
 
Publisher Journal of Economics Bibliography
 
Contributor —
 
Date 2016-09-18
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://www.kspjournals.org/index.php/JEB/article/view/850
10.1453/jeb.v3i3.850
 
Source Journal of Economics Bibliography; Vol 3, No 3 (2016): September; 498-515
2149-2387
 
Language eng
 
Relation http://www.kspjournals.org/index.php/JEB/article/view/850/1048
 
Rights Copyright (c) 2016 Journal of Economics Bibliography
http://creativecommons.org/licenses/by-nc/4.0