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