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Nonparametric Identification of Differentiated Products Demand Using Micro Data

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  • Steven T. Berry
  • Philip A. Haile

Abstract

We examine identification of differentiated products demand when one has "micro data" linking individual consumers' characteristics and choices. Our model nests standard specifications featuring rich observed and unobserved consumer heterogeneity as well as product/market-level unobservables that introduce the problem of econometric endogeneity. Previous work establishes identification of such models using market-level data and instruments for all prices and quantities. Micro data provides a panel structure that facilitates richer demand specifications and reduces requirements on both the number and types of instrumental variables. We address identification of demand in the standard case in which non-price product characteristics are assumed exogenous, but also cover identification of demand elasticities and other key features when product characteristics are endogenous. We discuss implications of these results for applied work.

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  • Steven T. Berry & Philip A. Haile, 2022. "Nonparametric Identification of Differentiated Products Demand Using Micro Data," Papers 2204.06637, arXiv.org, revised Apr 2022.
    • Handle: RePEc:arx:papers:2204.06637
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    2. Miravete, Eugenio J. & Seim, Katja & Thurk, Jeff, 2023. "Pass-through and tax incidence in differentiated product markets," International Journal of Industrial Organization, Elsevier, vol. 90(C).
    3. Amandeep Singh & Ye Liu & Hema Yoganarasimhan, 2023. "Choice Models and Permutation Invariance: Demand Estimation in Differentiated Products Markets," Papers 2307.07090, arXiv.org, revised Feb 2024.
    4. Martin O'Connell & Pierre Dubois & Rachel Griffith, 2022. "The Use of Scanner Data for Economics Research," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 723-745, August.
    5. Howard Smith & Walter Beckert & Yuya Takahashi, 2020. "Competition in a spatially-differentiated product market with negotiated prices," Economics Series Working Papers 921, University of Oxford, Department of Economics.
    6. Rachel Griffith & Martin O’Connell & Kate Smith, 2022. "Price Floors and Externality Correction," The Economic Journal, Royal Economic Society, vol. 132(646), pages 2273-2289.
    7. Pietro Tebaldi & Alexander Torgovitsky & Hanbin Yang, 2023. "Nonparametric Estimates of Demand in the California Health Insurance Exchange," Econometrica, Econometric Society, vol. 91(1), pages 107-146, January.
    8. Fabian Dunker & Stefan Hoderlein & Hiroaki Kaido, 2023. "Nonparametric identification of random coefficients in aggregate demand models for differentiated products," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 279-306.
    9. Nail Kashaev, 2018. "Identification and estimation of multinomial choice models with latent special covariates," Papers 1811.05555, arXiv.org, revised Mar 2022.
    10. Kandelhardt, Johannes, 2023. "Flexible estimation of random coefficient logit models of differentiated product demand," DICE Discussion Papers 399, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    11. Diego Jimenez-Hernandez & Enrique Seira, 2022. "Should the government sell you goods? Evidence from the milk market in Mexico," Working Paper Series WP 2023-19, Federal Reserve Bank of Chicago.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L0 - Industrial Organization - - General

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