Seasonality on non-linear price effects in scanner-data based market-response models
Dennis Fok and
Philip Hans Franses
No EI 2005-45, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute
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, multi-level 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 in a limited number of yearly data and (ii) non-linear price effects due to historic reference prices. We discuss representation and inference and we propose an estimation method using Bayesian techniques. An illustration to a market-response model for 96 brands for about 8 years of weekly data shows the merits of our approach.
Keywords: Bayes estimation; MCMC; non-linearity; panels of time series; threshold models; weekly seasonality (search for similar items in EconPapers)
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Journal Article: Seasonality and non-linear price effects in scanner-data-based market-response models (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:ems:eureir:7032
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