To understand the relevance of marketing efforts, it has become standard practice to estimate the long-run and short-run effects of the marketing-mix, using, say, weekly scanner data. A common vehicle for this purpose is an econometric time series model. Issues that are addressed in the literature are unit roots, cointegration, structural breaks and impulse response functions. In this paper we summarize the most important concepts by reviewing all possible empirical cases that can be encountered in practice using a prototypical model. We provide guidelines for practitioners, and illustrate these for a detailed workedout example.

Additional Metadata
Keywords dynamic effects, econometric time series models, marketing mix
JEL Time-Series Models; Dynamic Quantile Regressions (jel C32), Statistical Decision Theory; Operations Research (jel C44), Business Administration and Business Economics; Marketing; Accounting (jel M), Marketing (jel M31)
Persistent URL
Series ERIM Report Series Research in Management
Horváth, C, & Franses, Ph.H.B.F. (2003). Deriving dynamic marketing effectiveness from econometric time series models (No. ERS-2003-079-MKT). ERIM Report Series Research in Management. Retrieved from