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Estimating Asymmetric Advertising Response: An Application to U.S. Nonalcoholic Beverage Demand

Published online by Cambridge University Press:  26 January 2015

Yuqing Zheng  and
Harry M. Kaiser
Yuqing Zheng
Affiliation:
Department of Applied Economics and Management at Cornell University, Ithaca, NY
Harry M. Kaiser
Affiliation:
Department of Applied Economics and Management at Cornell University, Ithaca, NY
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Abstract

We propose a regime-switching model that allows demand to respond asymmetrically to upward and downward advertising changes. With the introduction of a smooth transition function, the model features smooth rather than abrupt parameter changes between regimes. We apply the model to nonalcoholic beverage data in the United States for 1974 through 2005 to investigate asymmetric advertising response. Results indicate that a decrease in milk advertising had a more profound impact on milk demand than an increase did. An increase in milk advertising had no impact on milk demand, but a decrease could have an own-advertising elasticity up to 0.049.

Keywords

asymmetric advertising responsedemand systemnegative asymmetrynonalcoholic beverage demandpositive asymmetryregime switchingsmooth transition functionC32M37Q11
Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 40 , Issue 3 , December 2008 , pp. 837 - 849
Copyright
Copyright © Southern Agricultural Economics Association 2008

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References

Adachi, K., and Liu, D.J. “Estimating Threshold Effects of Generic Fluid Milk and Cheese Advertising.” Working Paper, Department of Applied Economics, University of Minnesota, 2006.Google Scholar
Alston, J.M., Chalfant, J.A., and Piggott, N.E. “Incorporating Demand Shifters in the Almost Ideal Demand System.” Economics Letters 70(January 2001):73–78.Google Scholar
Aykac., A., Corstjens., M., and Gautschi, D. “Is There a Kink in Your Advertising?” Journal of Advertising Research 24(1984):27–35.Google Scholar
Chung, C., and Kaiser, H.M. “Determinants of Temporal Variations in Generic Advertising Effectiveness.” Agribusiness 16(Spring 2000): 197–214.Google Scholar
Clarke, D. “Econometric Measurement of the Duration of Advertising Effect on Sales.” Journal of Marketing Research 13(November 1976) :345-57.Google Scholar
Enders, W. Applied Econometric Time Series, 2nd edn. Hoboken, NJ: John Wiley & Sons, 2004.Google Scholar
Hanssens, D.M., Parsons, L.J., and Schults, R.L. Market Response Models: Econometric and Time Series Analysis. Norwell, MA: Kluwer Academic Publishers, 1990.Google Scholar
Houck, J.P. “An Approach to Specifying and Estimating Nonreversible Functions.” American Journal of Agricultural Economics 59(August 1977) :570-72.Google Scholar
Kinnucan, H.W., and Forker, O.D. “Seasonality in the Consumer Response to Milk Advertising with Implications for Milk Promotion Policy.” American Journal of Agricultural Economics 68(August 1986): 562-71.Google Scholar
Kinnucan, H.W., Miao., Y., Xiao., H., and Kaiser, H.M. “Effects of Advertising on U.S. Nonalcoholic Beverage Demand: Evidence from a Two-Stage Rotterdam Model.” Advances in Applied Microeconomics, Baye, M.R. and Nelson, J.P. eds., pp. 1–30. Volume 10, Amsterdam: Elsevier Science, 2001.Google Scholar
Kinnucan, H.W., and Venkateswaran, M. “Generic Advertising and the Structural Heterogeneity Hypothesis.” Canadian Journal of Agricultural Economics 42(November 1994):381-96.CrossRefGoogle Scholar
Kinnucan, H.W., and Zheng, Y. “National Benefit-Cost Estimates for the Dairy, Beef, Pork and Cotton Promotion Programs.” The Economics of Commodity Promotion Programs: Lessons from California, Kaiser, H.M., Alston, J.M., Crespi, J. and Sexton, R.J. eds., pp. 255-84. New York: Peter Lang Publishing, 2005.Google Scholar
Little, J.D.C. “Aggregate Advertising Models: The State of the Art.” Operations Research 27(July-August 1979):629-67.Google Scholar
Manera, M., and Frey, G. “Econometric Models of Asymmetric Price Transmission.” Working Paper, International Energy Markets, 2005. Internet site: http://www.feem.it/Feem/Pub/Publications/WPapers/default.htm (Accessed May 1, 2007).CrossRefGoogle Scholar
Meyer, J., and Cramon-Taubadel, S.v. “Asymmetric Price Transmission: A Survey.” Journal of Agricultural Economics 55(November 2004): 581–611.CrossRefGoogle Scholar
Moschini, G. “Units of Measurement and the Stone Index in Demand System Estimation.” American Journal of Agricultural Economics 77(February 1995):63–68.Google Scholar
Moschini, G., and Vissa, A. “Flexible Specification of Mixed Demand Systems.” American Journal of Agricultural Economics 75(February 1993): 1–9.Google Scholar
Parsons, L.J. “A Rachet Model of Advertising Carryover Effects.” Journal of Marketing Research 13(February 1976):76–79.Google Scholar
Peltzman, S. “Prices Rise Faster than They Fall.” Journal of Political Economy 108(June 2000): 466–502.Google Scholar
Reberte, C., Kaiser, H.M., Lenz, J.E., and Forker, O.D. “Generic Advertising Wearout: The Case of the New York City Fluid Milk Campaign.” Journal of Agricultural and Resource Economics 21 (1996): 199–209.Google Scholar
Rubin, D.B. Multiple Imputation for Nonresponses in Surveys. New York: John Wiley, 1987.Google Scholar
Rubin, D.B. “Multiple Imputation after 18+ Years.” Journal of the American Statistical Association 91(June 1996):473-89.Google Scholar
Rubin, D.B., and Schenker, N. “Multiple Imputation for Interval Estimation From Simple Random Samples with Ignorable Nonre-sponse.” Journal of the American Statistical Association 81(June 1986):366-74.Google Scholar
Schmit, T.M., and Kaiser, H.M. “Decomposing the Variation in Generic Advertising Response over Time.” American Journal of Agricultural Economics 86(February 2004): 139-53.Google Scholar
Shao, J., and Sitter, R.R. “Bootstrap for Imputed Survey Data.” Journal of the American Statistical Association 91(September 1996): 1278-88.Google Scholar
Teräsvirta, T. “Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models.” Journal of the American Statistical Association 89(March 1994):208-18.Google Scholar
Tweeten, L.G., and Quance, C.L. “Positivistic Measures of Aggregate Supply Elasticities: Some New Approaches.” American Journal of Agricultural Economics 51 (May 1969):342-52.Google Scholar
Vande Kamp, P.R., and Kaiser, H.M. “Irreversibility in Advertising-Demand Response Functions: An Application to Milk.” American Journal of Agricultural Economics 81 (May 1999):385-96.Google Scholar
Vande Kamp, P.R., and Kaiser, H.M. “Optimal Temporal Policies in Fluid Milk Advertising.” American Journal of Agricultural Economics 82(May 2000):274-86.Google Scholar
van Dijk, D., Teräsvirta, T., and Franses, P.H. “Smooth Transition Autoregressive Models: A Survey of Recent Developments.” Econometric Reviews 21(April 2002):1–47.Google Scholar
Ward, R.W. “Asymmetry in Retail, Wholesale and Shipping Point Pricing for Fresh Vegetables.” American Journal of Agricultural Economics 62(May 1982):205-12.Google Scholar
Wolffram, R. “Positivistic Measures of Aggregate Supply Elasticities: Some New Approaches: Some Critical Notes.” American Journal of Agricultural Economics 53(May 1971):356-59.Google Scholar