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
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://repub.eur.nl/pub/7032/EI2005-45.pdf (application/pdf)

Related works:
Journal Article: Seasonality and non-linear price effects in scanner-data-based market-response models (2007)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ems:eureir:7032

Access Statistics for this paper

More papers in Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute Contact information at EDIRC.
Bibliographic data for series maintained by RePub ( this e-mail address is bad, please contact ).