Outlier robust analysis of long-run marketing effects for weekly scanning data
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.
|Keywords||cointegration LM-test, distribution, market share, outlier robust method, scanning data|
|JEL||Time-Series Models; Dynamic Quantile Regressions (jel C32), Marketing (jel M31)|
|Persistent URL||dx.doi.org/10.1016/S0304-4076(98)00065-7, hdl.handle.net/1765/13824|
|Journal||Journal of Econometrics|
Franses, Ph.H.B.F, 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