IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v244y2024i1s0304407624001994.html
   My bibliography  Save this article

Identification in discrete choice models with imperfect information

Author

Listed:
  • Gualdani, Cristina
  • Sinha, Shruti

Abstract

We study identification of preferences in static single-agent discrete choice models where decision makers may be imperfectly informed about the state of the world. Leveraging the notion of one-player Bayes Correlated Equilibrium by Bergemann and Morris (2016), we provide a tractable characterisation of the sharp identified set. We develop a procedure to practically construct the sharp identified set following a sieve approach, and provide sharp bounds on counterfactual outcomes of interest. Using our methodology and data on the 2017 UK general election, we estimate a spatial voting model under weak assumptions on agents’ information about the returns to voting. Counterfactual exercises quantify the consequences of imperfect information on the well-being of voters and parties.

Suggested Citation

  • Gualdani, Cristina & Sinha, Shruti, 2024. "Identification in discrete choice models with imperfect information," Journal of Econometrics, Elsevier, vol. 244(1).
    • Handle: RePEc:eee:econom:v:244:y:2024:i:1:s0304407624001994
    DOI: 10.1016/j.jeconom.2024.105854
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407624001994
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jeconom.2024.105854?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Discrete choice model; Bayesian persuasion; Bayes Correlated Equilibrium; Incomplete information; Partial identification; Moment inequalities; Spatial model of voting;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

    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:eee:econom:v:244:y:2024:i:1:s0304407624001994. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    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.