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Consumer Demand Estimation

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  • Merino Troncoso, Carlos

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.

Suggested Citation

  • Merino Troncoso, Carlos, 2021. "Consumer Demand Estimation," MPRA Paper 105169, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:105169
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    References listed on IDEAS

    as
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    4. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    5. E. J. Working, 1927. "What Do Statistical "Demand Curves" Show?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 41(2), pages 212-235.
    6. Peter Davis & Eliana Garcés, 2009. "Quantitative Techniques for Competition and Antitrust Analysis," Economics Books, Princeton University Press, edition 1, number 9078.
    7. Merino Troncoso, Carlos, 2019. "Market power and welfare loss," MPRA Paper 96902, University Library of Munich, Germany.
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    9. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Machine Learning Methods for Demand Estimation," American Economic Review, American Economic Association, vol. 105(5), pages 481-485, May.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Demand; Consumer; Elasticity; Random Coefficient Model;
    All these keywords.

    JEL classification:

    • L40 - Industrial Organization - - Antitrust Issues and Policies - - - General
    • L44 - Industrial Organization - - Antitrust Issues and Policies - - - Antitrust Policy and Public Enterprise, Nonprofit Institutions, and Professional Organizations

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