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Generalized maximum entropy estimation of discrete choice models

Author

Listed:
  • Paul Corral

    (American University)

  • Mungo Terbish

    (American University)

Abstract

In this article, we describe the gmentropylogit command, which implements the generalized maximum entropy estimation methodology for discrete choice models. This information theoretic procedure is preferred over its maximum likelihood counterparts because it is more efficient, avoids strong parametric assumptions, works well when the sample size is small, performs well when the covariates are highly correlated, and functions well when the matrix is ill conditioned. Here we introduce the generalized maximum entropy. Copyright 2015 by StataCorp LP.

Suggested Citation

  • Paul Corral & Mungo Terbish, 2015. "Generalized maximum entropy estimation of discrete choice models," Stata Journal, StataCorp LP, vol. 15(2), pages 512-522, June.
    • Handle: RePEc:tsj:stataj:v:15:y:2015:i:2:p:512-522
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    Cited by:

    1. Ackerman, Adam, 2022. "The effect of combat exposure on sexually transmitted diseases," Economics & Human Biology, Elsevier, vol. 46(C).
    2. Clara Cicatiello & Luca Secondi & Ludovica Principato, 2019. "Investigating Consumers’ Perception of Discounted Suboptimal Products at Retail Stores," Resources, MDPI, vol. 8(3), pages 1-10, July.
    3. Fernandez-Dominguez Amilcar Orlian, 2020. "Effect of Actual and Perceived Violence on Internal Migration: Evidence from Mexico’s Drug War," IZA Journal of Development and Migration, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 11(1), pages 1-27, January.
    4. Luca Secondi, 2019. "Expiry Dates, Consumer Behavior, and Food Waste: How Would Italian Consumers React If There Were No Longer “Best Before” Labels?," Sustainability, MDPI, vol. 11(23), pages 1-15, December.

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