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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

Abstract: 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)
Date: 2005-01-01
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Journal Article: Seasonality and non-linear price effects in scanner-data-based market-response models (2007) Downloads
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