An Empirical Model of Advertising Dynamics
This paper develops a model of dynamic advertising competition, and applies it to the problem of optimal advertising scheduling through time. In many industries we observe advertising “pulsing”, whereby firms systematically switch advertising on and off at a high-frequency. Hence, we observe periods of zero and non-zero advertising, as opposed to a steady level of positive advertising. Previous research has rationalized pulsing through two features of the sale response function: an S-shaped response to advertising, and long-run effects of current advertising on demand. Despite considerable evidence for advertising carry-over, existing evidence for non-convexities in the shape of the sales-response to advertising has been limited and, often, mixed. We show how both features can be included in a discrete choice based demand system and estimated using a simple partial maximum likelihood estimator. The demand estimates are then taken to the supply side, where we simulate the outcome of a dynamic game using the Markov perfect equilibrium (MPE) concept. Our objective is not to test for the specific game generating observed advertising levels. Rather, we wish to verify whether the use of pulsing (on and off) can be justified as an equilibrium advertising practice. We solve for the equilibrium using numerical dynamic programming methods. The flexibility provided by the numerical solution method allows us to improve on the existing literature, which typically considers only two competitors, and places strong restrictions on the demand models for which the supply side policies can be obtained. We estimate the demand model using data from the Frozen Entree product category. We find evidence for a threshold effect, which is qualitatively similar to the aforementioned S-shaped advertising response. We also show that the threshold is robust to functional form assumptions for the marginal impact of advertising on demand. Our estimates, which are obtained without imposing any supply side restrictions, imply that firms should indeed pulse in equilibrium. Predicted advertising in the MPE is higher, on average, than observed advertising. On average, the optimal advertising policies yield a moderate profit improvement over the profits under observed advertising. Copyright Springer Science + Business Media, Inc. 2005
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Nevo, Aviv, 1998.
"Measuring Market Power in the Ready-To-Eat Cereal Industry,"
Food Marketing Policy Center Research Reports
037, University of Connecticut, Department of Agricultural and Resource Economics, Charles J. Zwick Center for Food and Resource Policy.
- Nevo, Aviv, 2001. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Econometrica, Econometric Society, vol. 69(2), pages 307-42, March.
- Nevo, Aviv, 1999. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Competition Policy Center, Working Paper Series qt7cm5p858, Competition Policy Center, Institute for Business and Economic Research, UC Berkeley.
- Nevo, Aviv, 1998. "Measuring Market Power in the Ready-To-Eat Cereal Industry," Research Reports 25164, University of Connecticut, Food Marketing Policy Center.
- Aviv Nevo, 1998. "Measuring Market Power in the Ready-to-Eat Cereal Industry," NBER Working Papers 6387, National Bureau of Economic Research, Inc.
- Aviv Nevo, 2003. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Microeconomics 0303006, EconWPA.
- Demetrios Vakratsas & Fred M. Feinberg & Frank M. Bass & Gurumurthy Kalyanaram, 2004. "The Shape of Advertising Response Functions Revisited: A Model of Dynamic Probabilistic Thresholds," Marketing Science, INFORMS, vol. 23(1), pages 109-119, April.
- White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
- Ariel Pakes & Paul McGuire, 1992.
"Computing Markov Perfect Nash Equilibria: Numerical Implications of a Dynamic Differentiated Product Model,"
NBER Technical Working Papers
0119, National Bureau of Economic Research, Inc.
- Ariel Pakes & Paul McGuire, 1994. "Computing Markov-Perfect Nash Equilibria: Numerical Implications of a Dynamic Differentiated Product Model," RAND Journal of Economics, The RAND Corporation, vol. 25(4), pages 555-589, Winter.
- Ariel Pakes & Paul McGuire, 1992. "Computing Markov perfect Nash equilibria: numerical implications of a dynamic differentiated product model," Discussion Paper / Institute for Empirical Macroeconomics 58, Federal Reserve Bank of Minneapolis.
- Vijay Mahajan & Eitan Muller, 1986. "Advertising Pulsing Policies for Generating Awareness for New Products," Marketing Science, INFORMS, vol. 5(2), pages 89-106.
- Ulrich Doraszelski & Mark Satterthwaite, 2003. "Foundations of Markov-Perfect Industry Dynamics. Existence, Purification, and Multiplicity," Discussion Papers 1383, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Ericson, Richard & Pakes, Ariel, 1995. "Markov-Perfect Industry Dynamics: A Framework for Empirical Work," Review of Economic Studies, Wiley Blackwell, vol. 62(1), pages 53-82, January.
- Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
- Pradeep Chintagunta & Jean-Pierre Dubé & Khim Yong Goh, 2005. "Beyond the Endogeneity Bias: The Effect of Unmeasured Brand Characteristics on Household-Level Brand Choice Models," Management Science, INFORMS, vol. 51(5), pages 832-849, May.
- J. Miguel Villas-Boas & Russell S. Winer, 1999. "Endogeneity in Brand Choice Models," Management Science, INFORMS, vol. 45(10), pages 1324-1338, October.
- Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-90, July.
- Naufel J. Vilcassim & Vrinda Kadiyali & Pradeep K. Chintagunta, 1999. "Investigating Dynamic Multifirm Market Interactions in Price and Advertising," Management Science, INFORMS, vol. 45(4), pages 499-518, April.
- Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, March.
- Chen, Xiaoheng & Conley, Timothy G., 2001. "A new semiparametric spatial model for panel time series," Journal of Econometrics, Elsevier, vol. 105(1), pages 59-83, November.
- Jeffrey M. Wooldridge, 2001.
"Econometric Analysis of Cross Section and Panel Data,"
MIT Press Books,
The MIT Press,
edition 1, volume 1, number 0262232197, June.
- Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, June.
- Rust, John, 1996. "Numerical dynamic programming in economics," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 14, pages 619-729 Elsevier.
- Roberts, M.J. & Samuelson, L., 1988.
"An Empirical Analysis Of Dynamic, Non-Price Competition In An Oligopolistic Industry,"
3-88-14, Pennsylvania State - Department of Economics.
- Mark J. Roberts & Larry Samuelson, 1988. "An Empirical Analysis of Dynamic, Nonprice Competition in an Oligopolistic Industry," RAND Journal of Economics, The RAND Corporation, vol. 19(2), pages 200-220, Summer.
- Hugo Benitez-Silva & John Rust & Gunter Hitsch & Giorgio Pauletto & George Hall, 2000. "A Comparison Of Discrete And Parametric Methods For Continuous-State Dynamic Programming Problems," Computing in Economics and Finance 2000 24, Society for Computational Economics.
- Slade, Margaret E, 1995. "Product Rivalry with Multiple Strategic Weapons: An Analysis of Price and Advertising Competition," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 4(3), pages 445-76, Fall.
- Ackerberg, Daniel A, 2001. "Empirically Distinguishing Informative and Prestige Effects of Advertising," RAND Journal of Economics, The RAND Corporation, vol. 32(2), pages 316-33, Summer.
- J. Miguel Villas-Boas, 1993. "Predicting Advertising Pulsing Policies in an Oligopoly: A Model and Empirical Test," Marketing Science, INFORMS, vol. 12(1), pages 88-102.
When requesting a correction, please mention this item's handle: RePEc:kap:qmktec:v:3:y:2005:i:2:p:107-144. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn)or (Christopher F. Baum)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.