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Individual counterfactuals with multidimensional unobserved heterogeneity

Author

Listed:
  • Richard Blundell

    (Institute for Fiscal Studies and University College London)

  • Dennis Kristensen

    (Institute for Fiscal Studies and University College London)

  • Rosa Matzkin

    (Institute for Fiscal Studies and UCLA)

Abstract

New nonparametric methods that identify and estimate counterfactuals for individuals, when each is characterized by a vector of unobserved characteristics, are developed and applied to estimate systems of individual consumer demand and welfare measures. The unobserved characteristics are allowed to enter in unrestricted ways. Identification is delivered through two fundamental assumptions: First, the system is invertible in the vector of unobserved heterogeneity. Second, there exist external, individual-specific, covariates that are related to the unobserved heterogeneity and do not enter directly into the system of interest. The observed external variables can be either discrete or continuously distributed. Estimators based on the identifying restrictions are developed and their asymptotic properties derived. Using UK micro data on consumer demand, we apply the methods to estimate individual demand counterfactuals subject to revealed preference inequalities.

Suggested Citation

  • Richard Blundell & Dennis Kristensen & Rosa Matzkin, 2017. "Individual counterfactuals with multidimensional unobserved heterogeneity," CeMMAP working papers CWP60/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:60/17
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    References listed on IDEAS

    as
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    Cited by:

    1. Haruki Kono, 2024. "Local Identification in Instrumental Variable Multivariate Quantile Regression Models," Papers 2401.11422, arXiv.org, revised Feb 2024.
    2. Aguiar, Victor H. & Kashaev, Nail & Allen, Roy, 2023. "Prices, profits, proxies, and production," Journal of Econometrics, Elsevier, vol. 235(2), pages 666-693.
    3. Yuichi Kitamura & Jörg Stoye, 2018. "Nonparametric Analysis of Random Utility Models," Econometrica, Econometric Society, vol. 86(6), pages 1883-1909, November.
    4. Roy Allen & John Rehbeck, 2020. "Counterfactual and Welfare Analysis with an Approximate Model," Papers 2009.03379, arXiv.org.
    5. Roy Allen, 2019. "Injectivity and the Law of Demand," Papers 1908.05714, arXiv.org.
    6. Steven T. Berry & Philip A. Haile, 2021. "Foundations of Demand Estimation," Cowles Foundation Discussion Papers 2301, Cowles Foundation for Research in Economics, Yale University.
    7. Allen, Roy, 2022. "Injectivity and the law of demand," Economics Letters, Elsevier, vol. 215(C).
    8. Christopher Dobronyi & Christian Gouri'eroux, 2020. "Consumer Theory with Non-Parametric Taste Uncertainty and Individual Heterogeneity," Papers 2010.13937, arXiv.org, revised Jan 2021.
    9. Hubner, Stefan, 2023. "Identification of unobserved distribution factors and preferences in the collective household model," Journal of Econometrics, Elsevier, vol. 234(1), pages 301-326.

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    More about this item

    Keywords

    simultaneous equations; nonseparable models; constructive identification; nonparametric methods; consumer behaviour; structural demand functions; revealed preference; bounds;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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