IDEAS home Printed from https://ideas.repec.org/a/aea/aejmic/v12y2020i1p42-74.html
   My bibliography  Save this article

Mutually Consistent Revealed Preference Demand Predictions

Author

Listed:
  • Abi Adams

Abstract

Revealed preference restrictions are increasingly used to predict demand behavior at new budgets of interest and as shape restrictions in nonparametric estimation exercises. However, the restrictions imposed are not sufficient for rationality when predictions are made at multiple budgets. I highlight the non-convexities in the set of predictions that arise when making multiple predictions. I develop a mixed integer programming characterization of the problem that can be used to impose rationality on multiple predictions. The approach is applied to the UK Family Expenditure Survey to recover rational demand predictions with substantially reduced computational resources compared to known alternatives.

Suggested Citation

  • Abi Adams, 2020. "Mutually Consistent Revealed Preference Demand Predictions," American Economic Journal: Microeconomics, American Economic Association, vol. 12(1), pages 42-74, February.
    • Handle: RePEc:aea:aejmic:v:12:y:2020:i:1:p:42-74
    DOI: 10.1257/mic.20150216
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/mic.20150216
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/mic.20150216.data
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/mic.20150216.ds
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    File URL: https://libkey.io/10.1257/mic.20150216?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Thomas Demuynck & John Rehbeck, 2023. "Computing revealed preference goodness-of-fit measures with integer programming," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 76(4), pages 1175-1195, November.

    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

    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:aea:aejmic:v:12:y:2020:i:1:p:42-74. 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: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    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.