Endogeneity and Exogeneity in Sales Response Functions

Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Endogeneity and exogeneity are topics that are mainly discussed in macroeconomics. We show that sales response functions (SRF) are exposed to the same problem if we assume that the control variables in a SRF reflect behavioral reactions of the supply side. The supply side actions are covering a flexible marketing component which could interact with the sales responses if sales managers decide to react fast according to new market situations. A recent article of Kao et al. (Evaluating the effectiveness of marketing expenditures, Working Paper, Ohio State University, Fisher College of Business, 2005) suggested to use a class of production functions under constraints to estimate the sales responses that are subject to marketing strategies. In this paper we demonstrate this approach with a simple SRF(1) model that contains one endogenous variable. Such models can be extended by further exogenous variables leading to SRF-X models. The new modeling approach leads to a multivariate equation system and will be demonstrated using data from a pharma-marketing survey in German regions.

Keywords

Exogenous Variable Marketing Effort Behavioral Equation Stochastic Partial Derivative Sale Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Baier D, Polasek W (2010) Marketing and regional sales: Evaluation of expenditure strategies by spatial sales response functions. In: Bock HH, Gaul W, Schader M, Bodendorf F, Bryant PG, Critchley F, Diday E, Ihm P, Meulmann J, Nishisato S, Ohsumi N, Opitz O, Radermacher FJ, Wille R, Locarek-Junge H, Weihs C (eds) Classification as a tool for research, studies in classification, data analysis, and knowledge organization. Springer Berlin Heidelberg, pp 673–681Google Scholar
  2. Chib S, Greenberg E (1995) Understanding the Metropolis-Hastings algorithm. Am Stat 49:327–335Google Scholar
  3. Kao LJ, Chiu CC, Gilbride T, Otter T, Allenby GM (2005) Evaluating the effectiveness of marketing expenditures. Working Paper, Ohio State University, Fisher College of BusinessGoogle Scholar
  4. Newton MA, Raftery AE (1994) Approximate Bayesian inference with the weighted likelihood bootstrap (with discussion). J Royal Stat Soc B 56:3–48MathSciNetMATHGoogle Scholar
  5. Polasek W (2010) Sales response functions (SRF) with stochastic derivative constraints. Working Paper, Institute of Advanced Studies, WienGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.IHSViennaAustria

Personalised recommendations