A comparative study of market share models using disaggregate data

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 of 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, the authors 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 suprior 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 vriables, and (4) attraction models predicted best overall. Moreover, in general, unconstrained models yielded superior forecasts relative to constrained models because brand-specific parameters were heteroÂgeneous for the product category tested.