Attraction versus Linear and Multiplicative Market Share Models: An Empirical Evaluation
比较了线性、乘法和吸引力市场份额模型在预测和描述上的表现，发现线性与乘法模型在特定条件下不逊于吸引力模型，并指出参数限制未能提升预测能力的原因。
Linear and multiplicative market share models are compared with the attraction specification on the basis of both predictive and descriptive measures. The data used represent 15 brands in three different markets. The results show that linear and multiplicative models with brand-specific parameters estimated by OLS or GLS may perform as well as or better than the attraction specification. The failure of parameter restrictions and ex-post normalization to improve prediction power is explained in terms of the heterogeneity in the estimated parameters. This finding is consistent across brands and models and suggests that the hypothesis of equal response parameters across brands should be tested formally before such restrictions are imposed. The combined results suggest that the attraction model may have considerable intuitive appeal, but that its superiority for descriptive and predictive purposes is not yet established.