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Abstract Details

Activity Number: 467
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Marketing
Abstract - #304426
Title: Interpretation of Shapley Value Regression Coefficients as Approximation for Coefficients Derived by Elasticity Criterion
Author(s): Stan Lipovetsky*+
Companies: GfK CRNA
Address: 8401 Golden Valley Rd., Minneapolis, MN, 55427, United States
Keywords: OLS ; multicollinearity ; regression coefficients ; Shapley value regression ; elasticity ; data gradients

Ordinary Least Squares (OLS) regression suggests the best aggregate of predictors to fit the data, but it was never designed to obtain meaningful coefficients of individual predictors. OLS coefficients are interpreted as a change in the dependent variable due to the unit change in a predictor subject to all the others being held constant, so it is an analogue of elasticity in absolute units. However, the obtained OLS coefficients could be very far from such elasticity values because multicollinearity can yield a negative sign for a presumably useful variable, thus, it is hardly possible to decide does it make sense to increase the value of such a variable to obtain a lift in the output. A very good approach to regressions with the interpretable coefficients and contributions to the explained variance is given by Shapley value regression (SVR), but special software is needed to construct

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