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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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    References listed on IDEAS

    as
    1. Tamás Bartus, 2005. "Estimation of marginal effects using margeff," Stata Journal, StataCorp LP, vol. 5(3), pages 309-329, September.
    2. Bartus, Tamas, 2005. "Estimation of marginal effects using margeff," Stata Journal, StataCorp LP, vol. 5(3), pages 1-21.
    3. Golan, Amos, 2008. "Information and Entropy Econometrics — A Review and Synthesis," Foundations and Trends(R) in Econometrics, now publishers, vol. 2(1–2), pages 1-145, February.
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    Cited by:

    1. 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, Open Access Journal, vol. 11(23), pages 1-15, December.

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