Seasonality and Non-linear Price Effects in Scanner-data based Market-response Models

Scanner data for fast moving consumer goods typically amount to panels of time series where both N and T are large. To reduce the number of parameters and to shrink parameters towards plausible and interpretable values, Hierarchical Bayes models turn out to be useful. Such models contain in the second level a stochastic model to describe the parameters in the first level. In this paper we propose such a model for weekly scanner data where we explicitly address (i) weekly seasonality when not many years of data are available and (ii) non-linear price effects due to historic reference prices. We discuss representation and inference and we propose a Markov Chain Monte Carlo sampler to obtain posterior results. An illustration to a market-response model for 96 brands for about 8 years of weekly data shows the merits of our approach.

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