A dynamic multinomial probit model for brand choice with different long-run and short-run effects of marketing-mix variables
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In this paper we propose a dynamic multinomial probit model in order to estimate the long-run and short- run effects of marketing mix variables on brand choice. The latent variables, which contain the unobserved perceived utilities, follow a first-order vector error correction autoregressive process of order 1 with current and lagged explanatory variables. The unrestricted autoregressive parameter matrix concerns the intertemporal correlation in perceived utilities of households over purchase occasions and indicates the persistence in brand choice. As explanatory variables we consider relative prices and promotional activities like feature and display. An important and novel feature of our model is that it allows for different long-run and short-run effects of promotional activities, thereby extending the models that are currently available in the literature. Additionally, to account for different base preferences for brands across households, we allow for consumer heterogeneity. Our application concerns a panel of households choosing among several brands of a FMCG. Our estimated model turns out to be an improvement over a static model and over a model with only short-run effects, in terms of in-sample fit and out-of-sample forecasts.