IDEAS home Printed from https://ideas.repec.org/a/oup/restud/v75y2008i4p1069-1080.html

Heterogeneity and the Non-Parametric Analysis of Consumer Choice: Conditions for Invertibility

Author

Listed:
  • Walter Beckert
  • Richard Blundell

Abstract

This paper considers structural non-parametric random utility models for continuous choice variables with unobserved heterogeneity. We provide sufficient conditions on random preferences to yield reduced-form systems of non-parametric stochastic demand functions that allow global invertibility between demands and non-separable unobserved heterogeneity. Invertibility is essential for global identification of structural consumer demand models, for the existence of well-specified probability models of choice and for the non-parametric analysis of revealed stochastic preference. We distinguish between new classes of models in which heterogeneity is separable and non-separable in the marginal rates of substitution, respectively. Copyright 2008, Wiley-Blackwell.

Suggested Citation

  • Walter Beckert & Richard Blundell, 2008. "Heterogeneity and the Non-Parametric Analysis of Consumer Choice: Conditions for Invertibility," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 75(4), pages 1069-1080.
    • Handle: RePEc:oup:restud:v:75:y:2008:i:4:p:1069-1080
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1467-937X.2008.00500.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Steven Berry & Amit Gandhi & Philip Haile, 2013. "Connected Substitutes and Invertibility of Demand," Econometrica, Econometric Society, vol. 81(5), pages 2087-2111, September.
    2. Romuald Meango, 2023. "Using Probabilistic Stated Preference Analyses to Understand Actual Choices," Papers 2307.13966, arXiv.org.
    3. Hubner, Stefan, 2016. "Topics in nonparametric identification and estimation," Other publications TiSEM 08fce56b-3193-46e0-871b-0, Tilburg University, School of Economics and Management.
    4. Sher, Itai & Kim, Kyoo il, 2014. "Identifying combinatorial valuations from aggregate demand," Journal of Economic Theory, Elsevier, vol. 153(C), pages 428-458.
    5. Cherchye, Laurens & Demuynck, Thomas & Rock, Bram De, 2019. "Bounding counterfactual demand with unobserved heterogeneity and endogenous expenditures," Journal of Econometrics, Elsevier, vol. 211(2), pages 483-506.
    6. Allen, Roy, 2022. "Injectivity and the law of demand," Economics Letters, Elsevier, vol. 215(C).
    7. Ian Crawford & Matthew Polisson, 2015. "Demand Analysis with Partially Observed Prices," Discussion Papers in Economics 15/12, Division of Economics, School of Business, University of Leicester, revised Dec 2016.
    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.
    10. Andreas Chai & Christian Kiedaisch & Nicholas Rohde, 2021. "The saturation of household spending diversity and emergent properties of representative households," DeFiPP Working Papers 2104, University of Namur, Development Finance and Public Policies.
    11. 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.
    12. Blundell, Richard & Kristensen, Dennis & Matzkin, Rosa, 2014. "Bounding quantile demand functions using revealed preference inequalities," Journal of Econometrics, Elsevier, vol. 179(2), pages 112-127.
    13. Arthur Lewbel & Krishna Pendakur, 2017. "Unobserved Preference Heterogeneity in Demand Using Generalized Random Coefficients," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 1100-1148.
    14. Andreas Chai & Christian Kiedaisch & Nicholas Rohde, 2017. "The saturation of spending diversity and the truth about Mr Brown and Mrs Jones," Discussion Papers in Economics economics:201701, Griffith University, Department of Accounting, Finance and Economics.
    15. Walter Beckert, 2007. "Specification and Identification of Stochastic Demand Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 669-683.
    16. Stefan Hoderlein & Jörg Stoye, 2015. "Testing stochastic rationality and predicting stochastic demand: the case of two goods," Economic Theory Bulletin, Springer;Society for the Advancement of Economic Theory (SAET), vol. 3(2), pages 313-328, October.
    17. Andreas Chai & Nicholas Rohde & Jacques Silber, 2015. "Measuring The Diversity Of Household Spending Patterns," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 423-440, July.
    18. Jun NAKABAYASHI & Yohsuke HIROSE, 2016. "Structural Estimation of the Scoring Auction Model," Discussion papers 16008, Research Institute of Economy, Trade and Industry (RIETI).
    19. Chai, Andreas & Kiedaisch, Christian & Rohde, Nicholas, 2024. "Household spending diversity, aggregation, and the value of product variety," Economic Modelling, Elsevier, vol. 141(C).

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D1 - Microeconomics - - Household Behavior

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:restud:v:75:y:2008:i:4:p:1069-1080. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/restud .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.