Consumer Demand Estimation

Carlos Merino Troncoso

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Demand; Consumer; Elasticity; Random Coefficient Model (search for similar items in EconPapers)
JEL-codes: L40 L44 (search for similar items in EconPapers)
Date: 2021-01-07
New Economics Papers: this item is included in nep-cmp
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