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The Inverse Product Differentiation Logit Model

Author

Listed:
  • Mogens Fosgerau
  • Julien Monardo
  • André de Palma

    (Université de Cergy-Pontoise, THEMA)

Abstract
We introduce the inverse product differentiation logit (IPDL) model, a micro-founded inverse market share model for differentiated products that captures market segmentation according to one or more characteristics. The IPDL model generalizes the nested logit model to allow richer substitution patterns, including complementarity in demand, and can be estimated by linear instrumental variables regression using market-level data. Furthermore, we provide Monte Carlo experiments that compare the IPDL model to the workhorse empirical models of the literature. Lastly, we show the empirical performance of the IPDL model using a well-known dataset on the ready-toeat cereals market.

Suggested Citation

  • Mogens Fosgerau & Julien Monardo & André de Palma, 2022. "The Inverse Product Differentiation Logit Model," THEMA Working Papers 2022-22, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    • Handle: RePEc:ema:worpap:2022-22
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    Cited by:

    1. Roy Allen & John Rehbeck, 2020. "Identification of Random Coefficient Latent Utility Models," Papers 2003.00276, arXiv.org.
    2. Wang, Ao, 2021. "A BLP Demand Model of Product-Level Market Shares with Complementarity," The Warwick Economics Research Paper Series (TWERPS) 1351, University of Warwick, Department of Economics.
    3. Allen, Roy, 2022. "Injectivity and the law of demand," Economics Letters, Elsevier, vol. 215(C).
    4. David Muller & Emerson Melo & Ruben Schlotter, 2023. "A Distributionally Robust Random Utility Model," Papers 2303.05888, arXiv.org.
    5. Soetevent, Adriaan R., 2021. "I’d Like to Move It! Consumption Rivalry in the EV Public Charging Market: Demand Estimation with Deterministic Choice Set Variation," EconStor Preprints 228520, ZBW - Leibniz Information Centre for Economics.
    6. Iaria, Alessandro & ,, 2020. "Identification and Estimation of Demand for Bundles," CEPR Discussion Papers 14363, C.E.P.R. Discussion Papers.
    7. Mogens Fosgerau & Emerson Melo & André de Palma & Matthew Shum, 2020. "Discrete Choice And Rational Inattention: A General Equivalence Result," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1569-1589, November.
    8. Leandro Benitez & German Coloma, 2022. "Estimación de demanda y simulación de concentraciones horizontales: el caso de Coca-Cola y AdeS en Argentina," Revista de Economía del Rosario, Universidad del Rosario, vol. 25(2), pages 1-22, December.
    9. Steven T. Berry & Philip A. Haile, 2021. "Foundations of Demand Estimation," NBER Working Papers 29305, National Bureau of Economic Research, Inc.
    10. Alessandro Iaria, & Wang, Ao, 2021. "An Empirical Model of Quantity Discounts with Large Choice Sets," The Warwick Economics Research Paper Series (TWERPS) 1378, University of Warwick, Department of Economics.
    11. Mogens Fosgerau & John Rehbeck, 2023. "Nontransitive Preferences and Stochastic Rationalizability: A Behavioral Equivalence," Papers 2304.14631, arXiv.org.

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    More about this item

    Keywords

    Demand estimation; Inverse demand; Logit; Consumer model; Differentiated products.;
    All these keywords.

    JEL classification:

    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L - Industrial Organization

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