We propose to estimate the parameters of the Market Share Attraction Model (Cooper and Nakanishi, 1988; Fok and Franses, 2004) in a novel way by using a nonparametric technique for function estimation called Support Vector Regressions (SVR) (Smola, 1996; Vapnik, 1995). Traditionally, the parameters of the Market Share Attraction Model are estimated via a Maximum Likelihood (ML) procedure, assuming that the data are drawn from a conditional Gaussian distribution. However, if the distribution is unknown, Ordinary Least Squares (OLS) estimation may seriously fail (Vapnik, 1982). One way to tackle this problem is to introduce a linear loss function over the errors and a penalty on the magnitude of model coefficients. This leads to qualities such as robustness to outliers and avoidance of the problem of overfitting. This kind of estimation forms the basis of the SVR technique, which, as we will argue, makes it a good candidate for estimating the Market Share Attraction Model. We test the SVR approach to predict (the evolution of) the market shares of 36 car brands simultaneously and report promising results.

Additional Metadata
Keywords market share attraction model, marketing, multi-output forecasting, shrinkage estimators, support vector regression
JEL Semiparametric and Nonparametric Methods (jel C14), Time-Series Models; Dynamic Quantile Regressions (jel C32), Neural Networks and Related Topics (jel C45), Forecasting and Other Model Applications (jel C53), Marketing (jel M31)
Persistent URL dx.doi.org/10.1080/07474938.2010.481989, hdl.handle.net/1765/21926
Series Econometric Institute Reprint Series
Journal Econometric Reviews
Nalbantov, G.I, Franses, Ph.H.B.F, Groenen, P.J.F, & Bioch, J.C. (2010). Estimating the Market Share Attraction Model using Support Vector Regressions. Econometric Reviews, 29(5), 688–716. doi:10.1080/07474938.2010.481989