Download Advanced econometric marketing models by Dennis Fox PDF

By Dennis Fox

ISBN-10: 9058920496

ISBN-13: 9789058920492

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T is defined by I K β kji xkjt . 2) allows the marketing instruments of brand 1 to influence the attractions of all incumbent brands in the post-introduction period. 2). 4, where we consider parameter estimation in a more flexible model, where the incumbent brands’ parameters are allowed to change. As usual, we assume a normal distribution for the errors. For the pre-introduction period we have (ε2t , . . , εIt ) ∼ N (0, Σ(1) ) and for the post-introduction period we have (ε1t , . . , εIt ) ∼ N (0, Σ(2) ), where the (1) and (2) superscripts denote parameters before and after the introduction, respectively.

These authors study the stochastic prediction of market shares using the multinomial logit model. In their paper they stress that in order to obtain accurate forecasts the uncertainty in parameter estimates should be taken into account. Upon using Monte Carlo experiments, they demonstrate that indeed the forecast accuracy improves when uncertainty is included. The market share attraction model differs from the multinomial logit model in an important aspect. The multinomial logit model obtains the market share as aggregated brand choice probabilities.

This (1) (2) ˜i , i = 2, . . , I − 1. One hypothesis corresponds to the parameter restriction µ ˜i = µ can also focus on the effect of a single specific marketing instrument, like the relative (1) (2) price. In that case, the test concerns β˜kji = β˜kji , i = 2, . . , I − 1 and j = 2, . . , I for a specific marketing instrument k. Finally, we can test for constant variance of the unexplained attractions of the incumbent brands. As stated before, the covariances can be interpreted as measuring the similarity of brands.

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