IDEAS home Printed from https://ideas.repec.org/p/hka/wpaper/20205-009.html
   My bibliography  Save this paper

An Information-Theoretic Approach to Partially Identified Problems

Author

Listed:
  • Amos Golan

    (American University and Santa Fe Institute)

  • Jeffrey Perloff

    (University of California, Berkeley)

Abstract

An information-theoretic maximum entropy (ME) model provides an alternative approach to finding solutions to partially identified models. In these models, we can identify only a solution set rather than point-identifying the parameters of interest, given our limited information. Manski (2021) proposed using statistical decision functions in general, and the minimax-regret (MMR) criterion in particular, to choose a unique solution. Using Manski's simulations for a missing data and a treatment problem, including an empirical example, we show that ME performs the same or better than MMR. In additional simulations, ME dominates various other statistical decision functions. ME has an axiomatic underpinning and is computationally efficient.

Suggested Citation

  • Amos Golan & Jeffrey Perloff, 2025. "An Information-Theoretic Approach to Partially Identified Problems," Working Papers 20205-009, Human Capital and Economic Opportunity Working Group.
    • Handle: RePEc:hka:wpaper:20205-009
    Note: MIP
    as

    Download full text from publisher

    File URL: http://humcap.uchicago.edu/RePEc/hka/wpaper/Golan_Perloff_information-theor-approach-part-ID.pdf
    File Function: First version, September 2025
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

    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:hka:wpaper:20205-009. 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: Jennifer Pachon (email available below). General contact details of provider: https://edirc.repec.org/data/mfichus.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.