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Advertising and Retail Promotion of Washington Apples: A Structural Latent Variable Approach to Promotion Evaluation

Published online by Cambridge University Press:  28 April 2015

Timothy J. Richards ,
X.M. Gao  and
Paul M. Patterson
Timothy J. Richards
Affiliation:
Morrison School of Agribusiness and Resource Management (MSABR), Arizona State University; AT&T, Jacksonville, FL; MSABR-ASU
X.M. Gao
Affiliation:
Morrison School of Agribusiness and Resource Management (MSABR), Arizona State University; AT&T, Jacksonville, FL; MSABR-ASU
Paul M. Patterson
Affiliation:
Morrison School of Agribusiness and Resource Management (MSABR), Arizona State University; AT&T, Jacksonville, FL; MSABR-ASU
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Abstract

“Commodity promotion” consists of many activities, each designed to contribute to a consumer's product knowledge or influence tastes. However, both knowledge and tastes are unobservable, or latent, variables influencing demand. This paper specifies a dynamic structural model of fresh fruit demand that treats promotion and other socioeconomic variables as “causal” variables influencing these latent variables. Estimating this state-space model using a Kalman filter approach provides estimates of both the system parameters and a latent variable series. The results show that these latent effects contribute positively to apple and other fruit consumption, while reducing banana consumption.

Keywords

commodity promotiondemand systemDYMIMICfresh fruitKalman filterLA/AIDSlatent variables
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 31 , Issue 1 , April 1999 , pp. 15 - 28
Copyright
Copyright © Southern Agricultural Economics Association 1999

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References

Aigner, D. J., Hsiao, C., Kapteyn, A., and Wans-beek, T.. “Latent Variable Models in Econometrics.” In Handbook of Econometrics, eds. Intriligator, M. and Griliches, Z.. Amsterdam: North Holland, 1982.Google Scholar
Alston, J. and Chalfant, J.. “The Silence of the Lambdas: A Test of the Almost Ideal and Rotterdam Models.” American Journal of Agricultural Economics 75(1993):304–313.CrossRefGoogle Scholar
Alston, J. M., Chalfant, J. A., Christian, J. E., Meng, E., and Piggott, N. E.. “The California Table Commission's Promotion Program: An Evaluation.” Department of Agricultural Economics, University of California, Davis, Davis, CA. 1996.Google Scholar
Blanciforti, L. and Green, R. “An Almost Ideal Demand System Incorporating Habits: An Analysis of Expenditures on Food and Aggregate Commodity Groups.” Review of Economic Statistics 65(1983): 1–10.CrossRefGoogle Scholar
Brumm, H. J. “Incentives in Incentive Contracting: An Application of the MIMIC Model.” Applied Economics 24(1992):337–345.CrossRefGoogle Scholar
Burmeister, E. and Wall, K. D. “Kalman Filtering Estimation of Unobserved Rational Expectations With An Application To The German Hyperinflation.” Journal of Econometrics. 20(1982): 255–84.CrossRefGoogle Scholar
Center for Nutrition Policy and Promotion. Washington, DC, personal communication, 1997.Google Scholar
Chang, H. S. and Kinnucan, H.. “Advertising and Structural Change in the Demand for Butter in Canada.” Canadian Journal of Agricultural Economics 38(1990):295–308.CrossRefGoogle Scholar
Chavas, J. P. “Structural Change in the Demand for Meat.” American Journal of Agricultural Economics 65(1983): 148–53.CrossRefGoogle Scholar
Chen, C.-F. “The EM Approach to the Multiple Indicators and Multiple Causes Model via the Estimation of the Latent Variable.” Journal of the American Statistical Association 76(1981):704–708.CrossRefGoogle Scholar
Chow, G. C. “Random and Changing Coefficient Models.” In Handbook of Econometrics, eds. Intriligator, M. and Griliches, Z.. Amsterdam: North Holland, 1982.Google Scholar
Cox, T. “A Rotterdam Model Incorporating Advertising Effects: The Case of Canadian Fats and Oils.” In Commodity Advertising and Promotion, eds. Kinnucan, H. W., Thompson, S. R., and Chang, H. S.. Ames: Iowa State Press, 1992.Google Scholar
Deaton, A. and Muellbauer, J.. “An Almost Ideal Demand System.” American Economics Review 70(1980):312–26.Google Scholar
Dempster, A. P., Laird, N. M., and Rubin, D. B.. “Maximum Likelihood From Incomplete Data Via the EM Algorithm.” Journal of Royal Statistical Society B39(1977):1–38.Google Scholar
Dixit, A. and Norman, A.. “Advertising and Welfare.” The Bell Journal of Economics 10(1978): 1–17.CrossRefGoogle Scholar
Engle, R. F., Lilien, D. M., and Watson, M. “A DYMIMIC Model of Housing Price Determination.” Journal of Econometrics 28(1985):307–326.CrossRefGoogle Scholar
Engle, R. F. and Watson, M.. “A One Factor Multivariate Time Series Model of Metropolitan Wage Rates.” Journal of American Statistics Association 76(1981):774–780.CrossRefGoogle Scholar
Erikson, G., Mitelhammer, R. C., Schotzko, R. T., and Seavert, C.. “An Evaluation of the Effectiveness of Pear Advertising and Promotion: Final Report.” Unpublished manuscript, Department of Agricultural Economics, Washington State University, 1997.Google Scholar
Gao, X. M. and Shonkwiler, J. S.. “Modeling Taste Change in Meat Demand: An Application of the MIMIC Model.” Paper presented at the SAEA Meetings, February 1991.Google Scholar
Gao, X. M. “Dynamic Taste Change in Meat Demand: An Application of the DYMIMIC Model.” Paper presented at the AAEA Meetings, July 1992.Google Scholar
Goddard, E. W. and Cozzarin, B.. “A Preliminary Look at Advertising Beef, Pork, Chicken, Turkey, Eggs, Milk, Butter, Cheese, and Margarine in Canada.” In Commodity Advertising and Promotion, eds. Kinnucan, H. W., Thompson, S. R., and Chang, H. S.. Ames: Iowa State Press, 1992.Google Scholar
Goldberger, A. S. “Maximum-Likelihood Estimation of Regression Containing Unobservable Independent Variables.” International Economics Review 13(1972a):1–15.CrossRefGoogle Scholar
Goldberger, A. S. “Structural Equation Methods in Social Sciences.” Econometrica 40(1972b): 979–1002.CrossRefGoogle Scholar
Goldberger, A. S. “Maximum Likelihood Estimation of Regressions Containing Unobservable Independent Variables.” In Aigner, D. J. and Goldberger, A. S., eds. Latent Variables in Socioeconomic Models, New York, NY: Academic Press, 1977.Google Scholar
Green, R. “Dynamic Utility Functions for Measuring Advertising Response.” Research on Effectiveness of Agricultural Commodity Promotion. Proceedings from seminar, Arlington, VA, April 9–10, 1985.Google Scholar
Green, R. D., Carman, H. F., and McManus, K.. “Some Empirical Methods of Estimating Advertising Effects in Demand Systems: An Applications to Dried Fruits.” Western Journal of Agricultural Economics 16(1991):63–71.Google Scholar
Harvey, A. C.Structural Time Seris Analysis and the Kalman Filter. Boston, MA: Cambridge University Press. 1989.Google Scholar
Joreskog, K. G. and Goldberger, A. S.. “Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable.” Journal of the American Statistical Association 70(1975):631–639.Google Scholar
Kotowitz, Y. and Mathewson, F.. “Advertising, Consumer Information, and Product Quality.” The Bell Journal of Economics 10(1979):566–588.CrossRefGoogle Scholar
Lee, J-Y., Brown, M. G., and Seale, J. L. Jr. “Demand Relationships Among Fresh Fruit and Juices in Canada.” Review of Agricultural Economics 14(1992):255–262.CrossRefGoogle Scholar
Lee, J-Y., Seale, J. L. Jr., and Jierwiriyapant, P. A.. “Do Trade Agreements Help US Exports? A Study of the Japanese Citrus Industry.” Agribusiness 6(1990):505–514.3.0.CO;2-G>CrossRefGoogle Scholar
Nelson, P. “Advertising as Information.” Journal of Political Economy 82(1974):729–754.CrossRefGoogle Scholar
Pollak, R. A. and Wales, T. J.. “Comparison of the Quadratic Expenditure System and Translog Demand System with Alternative Specifications of Demographic Effects.” Econometrica 48(1980): 595–612.CrossRefGoogle Scholar
Robins, P. K. and West, R. W.. “Measurement Errors in the Estimation of Home Value.” Journal of the American Statistical Association 72(1977): 290–294.CrossRefGoogle Scholar
Rossi, N. “Budget Share Demographic Translation and The Aggregate Almost Ideal Demand System.” European Economic Review. 31(1988): 1301–1318.CrossRefGoogle Scholar
Senauer, B., Asp, E., and Kinsey, J.. Food Trends and the Changing Consumer. St. Paul, MN: Eagan Press, 1991.Google Scholar
Stigler, G. and Becker, G.. “De Gustibus Non Est Disputandum.“ American Economics Review 67(1977):76–90.Google Scholar
Tegene, A. “The Kalman Filter Approach for Testing Structural Change in the Demand for Alcoholic Beverages in the U.S.” Applied Economics 22(1990): 1407–1416.CrossRefGoogle Scholar
U. S. Department of Agriculture. Economic Research Service. Food Consumption, Prices, and Expenditure. Washington, DC, various issues.Google Scholar
U. S. Department of Agriculture. Economic Research Service. Fruit and Tree Nuts: Yearbook. Washington, DC, various issues.Google Scholar
U. S. Department of Labor. Bureau of Labor Statistics. Handbook of Labor Statistics. Washington DC, various issues.Google Scholar
Watson, M. W. and Engle, R.F.. “Alternative Algorithms For The Estimation of Dynamic Factor, MIMIC and Varying Coefficient Regression Models.” Journal of Econometrics 23(1983): 385–400.CrossRefGoogle Scholar
Watson, P. K. “Kalman Filtering as an Alternative to Ordinary Least Squares—Some Theoretical Considerations and Empirical Results.” Empirical Economics 8(1983):71–85.CrossRefGoogle Scholar
Veeman, M. and Xu, X.. “Model Choice and Structural Specification for Canadian Meat Consumption: 1967–1992.” Unpublished manuscript, Department of Rural Economy, University of Alberta, Edmonton, Alberta, Canada, 1995.Google Scholar