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The Inverse Product Differentiation Logit Model

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  • André De Palma
  • Mogens Fosgerau
  • Julien Monardo

    (Université de Cergy-Pontoise, THEMA)

Abstract

We propose the Inverse Product Differentiation Logit (IPDL) model, a structural (inverse) demand model for differentiated products that captures market segmentation with segments that may overlap in any way. The IPDL model generalizes the nested logit model to allow richer substitution patterns, including complementarity in demand, and can be estimated by linear instrumental variable regression using aggregate data. We use the IPDL model to estimate the demand for cereals in Chicago. We then extend it to a general demand model that is consistent with a utility model of heterogeneous, utilitymaximizing consumers.

Suggested Citation

  • André De Palma & Mogens Fosgerau & Julien Monardo, 2021. "The Inverse Product Differentiation Logit Model," THEMA Working Papers 2021-04, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    • Handle: RePEc:ema:worpap:2021-04
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    Cited by:

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    2. 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.
    3. Steven T. Berry & Philip A. Haile, 2021. "Foundations of Demand Estimation," Cowles Foundation Discussion Papers 2301, Cowles Foundation for Research in Economics, Yale University.
    4. Roy Allen & John Rehbeck, 2020. "Identification of Random Coefficient Latent Utility Models," Papers 2003.00276, arXiv.org.
    5. 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.
    6. Allen, Roy, 2022. "Injectivity and the law of demand," Economics Letters, Elsevier, vol. 215(C).
    7. 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.
    8. Iaria, Alessandro & ,, 2020. "Identification and Estimation of Demand for Bundles," CEPR Discussion Papers 14363, C.E.P.R. Discussion Papers.
    9. 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.
    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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    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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