We consider econometric modeling of weekly observed scanning data on a fast moving consumer good (FMCG), with a specific focus on the relationship between market share, distribution, advertising, price, and promotion. Such data can show non-stationary characteristics. Therefore, we use cointegration techniques to quantify the long-run effects of marketing efforts. Since weekly scanning data can contain aberrant observations due to, e.g., out-of-stock situations or measurement errors, we favor an outlier robust cointegration method, which we outline in detail. In our illustrative FMCG example, we find different results across robust and non-robust methods for the long-run marketing effects.

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doi.org/10.1016/S0304-4076(98)00065-7, hdl.handle.net/1765/13824
Journal of Econometrics
Erasmus School of Economics

Franses, P. H., Kloek, T., & Lucas, A. (1998). Outlier robust analysis of long-run marketing effects for weekly scanning data. Journal of Econometrics, 293–315. doi:10.1016/S0304-4076(98)00065-7