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Deriving dynamic marketing effectiveness from econometric time series models

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Author Info
Horváth, C.
Franses, Ph.H.B.F. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University)
Abstract

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.

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Paper provided by Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam. in its series Research Paper with number ERS-2003-079-MKT Revision_Date: 2009-11-06.

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Date of creation: 01 Jan 2003
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Handle: RePEc:dgr:eureri:30001132

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Related research
Keywords: dynamic effects; marketing mix; econometric time series models;

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  1. Kristian S. Palda, 1964. "The Measurement of Cumulative Advertising Effects," Journal of Business, University of Chicago Press, vol. 38, pages 162. [Downloadable!]
  2. G. Dekimpe, Marnik & Hanssens, Dominique M. & Silva-Risso, Jorge M., 1998. "Long-run effects of price promotions in scanner markets," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 269-291, November. [Downloadable!] (restricted)
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