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Publication |
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The ING faculty has published
following article. Please contact us for further details. |
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Abstract |
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FORECASTING
PERFORMANCE OF MARKET SHARE MODELS: AN ASSESSMENT, ADDITIONAL
INSIGHTS, AND GUIDELINES
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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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