A
COMPARATIVE STUDY OF MARKET SHARE MODELS USING DISAGGREGATE DATA
by V. Kumar and Timothy B. Heath
Prior research assessing the predictive validity of alternate
market share models produced conflicting results and often found
that econometric models performed worse than naïve 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 naïve form for linear, multiplicative, and attraction specifications
across different levels of parameterization. Consistent with specification-based
arguments (1) econometric models were superior to naïve 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.
International
Journal of Forecasting; Vol. 06 (Year 1990)
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