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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.

Suggested Citation

  • Mishra, SK, 2016. "Shapley value regression and the resolution of multicollinearity," MPRA Paper 72116, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:72116
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    References listed on IDEAS

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    1. Sudhanshu K Mishra, 2013. "Global Optimization of Some Difficult Benchmark Functions by Host-Parasite Coevolutionary Algorithm," Economics Bulletin, AccessEcon, vol. 33(1), pages 1-18.
    2. Veronika Ročková & Edward George, 2014. "Negotiating multicollinearity with spike-and-slab priors," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 217-229, August.
    3. Mishra, SK, 2007. "Performance of Differential Evolution Method in Least Squares Fitting of Some Typical Nonlinear Curves," MPRA Paper 4634, University Library of Munich, Germany.
    4. Sudhanshu Mishra, 2004. "Multicollinearity and maximum entropy leuven estimator," Economics Bulletin, AccessEcon, vol. 3(25), pages 1-11.
    5. Mishra, SK, 2004. "Estimation under Multicollinearity: Application of Restricted Liu and Maximum Entropy Estimators to the Portland Cement Dataset," MPRA Paper 1809, University Library of Munich, Germany.
    6. Gikuang Jeff Chen, 2012. "A simple way to deal with multicollinearity," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 1893-1909, May.
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    Citations

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    Cited by:

    1. Mishra, Sudhanshu K, 2018. "A Simultaneous Equation Model of Globalization, Corruption, Democracy, Human Development and Social Progress," MPRA Paper 84213, University Library of Munich, Germany.
    2. Rodolfo Metulini & Giorgio Gnecco, 2023. "Measuring players’ importance in basketball using the generalized Shapley value," Annals of Operations Research, Springer, vol. 325(1), pages 441-465, June.
    3. Sudhanshu K. Mishra, 2018. "A Study on Regime Type and Globalization in Simultaneous Equation Framework," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 2(1), pages 99-128.
    4. Sudhanshu K. MISHRA, 2016. "BA Note on Construction of a Composite Index by Optimization of Shapley Value Shares of the Constituent Variables," Turkish Economic Review, KSP Journals, vol. 3(3), pages 466-472, September.
    5. Sudha Narayanan & Udayan Rathore & Mohit Sharma, 2019. "Women's nutritional empowerment and their well-being Identifying key drivers in India and Bangladesh," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2019-004, Indira Gandhi Institute of Development Research, Mumbai, India.
    6. Mishra, SK, 2017. "A New Kind of Two-Stage Least Squares Based on Shapley Value Regression," MPRA Paper 83534, University Library of Munich, Germany.
    7. Erin Lentz & Elizabeth Bageant & Sudha Narayanan, 2021. "Empowerment and nutrition in Niger: insights from the Women’s Empowerment in Nutrition grid," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(5), pages 1227-1244, October.
    8. Riccardo Colini-Baldeschi & Marco Scarsini & Stefano Vaccari, 2018. "Variance Allocation and Shapley Value," Methodology and Computing in Applied Probability, Springer, vol. 20(3), pages 919-933, September.

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    More about this item

    Keywords

    Multicollinearity; Shapley value; regression; computational algorithm; computer program; Fortran;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C71 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Cooperative Games

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