Consumer Demand Estimation

Merino Troncoso, Carlos (2021): Consumer Demand Estimation. Forthcoming in:

Abstract

In this chapter I will review the main methodologies used in economics for demand estimation, focusing on recent trends such as the structural approach and machine learning techniques. As one can imagine the literature review is extensive so due to space limitations I can only provide a summarized view of each theory. Nevertheless, the interested reader has a comprehensive bibliography at the end of the chapter for extensions and examples. There is also another barrier when explaining any concept in economics. Economics is widely based on Mathematics, Statistics and Econometrics so it is not possible to explain it without its usage. As it is not possible review econometrics and mathematics in this chapter I will refer to specific texts, and an appendix will give the reader a brief summary of the main concepts. Demand is usually the first step in the study of a market. Intuitively, suppliers only start production when they identify consumer interest in a particular good. All models reviewed try to solve the problems that traditionally have embarrassed demand estimation: identification, endogeneity and simultaneity. There is no perfect solution to them, each model has its advantages and limitations and are based on assumptions that are often irreal, so the model in itself is in all cases only an approximation of demand.

• Item Type: MPRA Paper
• Language: English
• Keywords: Demand, Consumer, Elasticity, Random Coefficient Model
• Subjects: L - Industrial Organization > L4 - Antitrust Issues and Policies > L40 - General; L - Industrial Organization > L4 - Antitrust Issues and Policies > L44 - Antitrust Policy and Public Enterprises, Nonprofit Institutions, and Professional Organizations

References

Ackerberg, D., Benkard, C. L., Berry , S., & Pakes, A. (2007). Econometric tools for analyzing market outcomes. In Handbook of econometrics (pp. 4171-4276). Elsevier.

Angrist, J. D., & Krueger, A. B. (2001). Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments. Journal of Economic Perspectives, 15(4), 69-85.

Bajari, P., Nekipelov, D., & Ryan, S. P. (2015). Machine Learning Methods for Demand Estimation. American Economic Review, 105(5), 481-85.

Belloni, A., Chernozhukov, V., & Hansen , C. (2014). High-Dimensional Methods and Inference on Structural and Treatment Effects. Journal of Economic Perspectives, 28(2), 1-23.

Berry, S., Levinston , J., & Pakes, A. (1995). Automobile Prices in Market Equilibrium. Econometrica, 63(4), 841-890. Bresnahan, T. F. (1987). Competition and Collusion in the American Automobile Industry: The 1955 Price War. The Journal of Industrial Economics, 35(4), 457-482.

Davis, P., & Garcés, E. (2009). Quantitative Techniques for Competition and Antitrust Analysis. Princeton University Press.

Deaton, A., & Muellbauer, J. (1980). An Almost Ideal Demand System. American Economic Review, 70(3), 312-326.

Marshall, A. (1890). Principles Of Economics; an Introductory Volume. London: Macmillan.

Mas-Collell, A., Whinston, M. D., & Green, J. (1995). Microeconomic Theory. Oxford University Press.

Merino Troncoso, C. (2019). Market power and welfare loss . Retrieved from mpra.ub.uni-muenchen.de.

Nevo, A. (2000). A practitioner's guide to estimation of random‐coefficients logit models of demand. Journal of economics & management strategy, 9(4), 513-548.

Rasmusen, & Rasmusen, E. (2006). The BLP Method of Demand Curve Estimation in Industrial Organization. Retrieved from http://www.rasmusen.org/papers/blp-rasmusen.pdf

Spence, A. (1976). Product Selection, Fixed Costs, and Monopolistic Competition. Review of Economic Studies, 43(2), 217-235.

Varian, H. (2014). Big data:New tricks of Econometrics. journal of Economic Perspectives, 28(2), 3-28.

Working, E. (1927). What Do Statistical "Demand Curves" Show? The Quarterly Journal of Economics, 212-235.

Working, H. (1925, August 1925). The Statistical Determination of Demand Curves. The Quarterly Journal of Economics, 39(4), 503-543.