FORECASTING
PERFORMANCE OF MARKET SHARE MODELS: AN ASSESSMENT, ADDITIONAL
INSIGHTS, AND GUIDELINES
by V. Kumar
The research provides an assessment of the relevant literature
on market share models and identifies the need for further research.
Additional insights are generated in this study by using point-of
-sale scanners for evaluating the forecasting performance of market
share models under various conditions. Specifically, weekly store-level
scanner data for four frequently purchased product categories-saltine
crackers, baking chips, diapers and toilet tissue, and simulated
data are used in this study. Consistent with theoretical expectations,
the attraction models estimated by GLS produce the best forecasts
even (1) at the brand level, and (2) when competitors' actions
are predicted. However, the superiority of the attraction models
is diminished when systematic errors are introduced to the values
of the competitors' predictor variables in the holdout sample.
In fact, naïve models outperform all types of econometric models
when large errors are present in the competitors' predictor values,
and among the econometric models, linear models produce better
forecasts than attraction models. The need for estimating the
models with GLS (as opposed to OLS) with the use of cross-sectional
time-series data is also illustrated. Finally, guidelines are
developed for practitioners and researchers on the usefulness
of market share models for forecasting.
International
Journal of Forecasting, Vol. 10 (Year 1994)
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