A comparative study of market share models using disaggregate data

Abstract : Prior research assessing the predictive validity of alternate market share models produced conflicting results and often found that econometric models performed worse than naive extrapolations. However, contributors to IJF's recent issue on market share models suggested that such models are often misspecified, in part because they exclude promotional variables and are estimated on aggregate data. Thus, we used weekly scanner data to assess full, reduced, and naive forms of linear, multiplicative, and attraction specifications across different levels of parameterization. Consistent with specification-based arguments, (1) econometric models were superior to naive models, (2) GLS estimates of attraction models were superior when models were fully specified, (3) OLS estimates of linear models were superior when models omitted important variables, and (4) attraction models predicted best overall. Moreover, in general, unconstrained models yielded superior forecasts relative to constrained models because brand-specific parameters were heterogeneous for the product category tested.
Type de document :
Article dans une revue
International Journal of Forecasting, Elsevier, 1990, vol. 6, issue 2, p. 163-174. 〈10.1016/0169-2070(90)90002-S〉
Liste complète des métadonnées

https://hal-hec.archives-ouvertes.fr/hal-00670544
Contributeur : Amaury Bouvet <>
Soumis le : mercredi 15 février 2012 - 16:05:04
Dernière modification le : mercredi 15 février 2012 - 16:05:04

Lien texte intégral

Identifiants

Collections

Citation

V. Kumar, Timothy B. Heath. A comparative study of market share models using disaggregate data. International Journal of Forecasting, Elsevier, 1990, vol. 6, issue 2, p. 163-174. 〈10.1016/0169-2070(90)90002-S〉. 〈hal-00670544〉

Partager

Métriques

Consultations de la notice

273