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Generalized maximum entropy estimation of linear models

Author

Listed:
  • Paul Corral

    (The World Bank)

  • Daniel Kuehn

    (Urban Institute)

  • Ermengarde Jabir

    (American University)

Abstract

In this article, we describe the user-written gmentropylinear command, which implements the generalized maximum entropy estimation method for linear models. This is an information-theoretic procedure preferable to its maximum likelihood counterparts in many applications; it avoids making distributional assumptions, works well when the sample is small or covariates are highly correlated, and is more efficient than its maximum likelihood equivalent. We give a brief introduction to the generalized maximum entropy procedure, present the gmentropylinear command, and give an example using the command.

Suggested Citation

  • Paul Corral & Daniel Kuehn & Ermengarde Jabir, 2017. "Generalized maximum entropy estimation of linear models," Stata Journal, StataCorp LP, vol. 17(1), pages 240-249, March.
    • Handle: RePEc:tsj:stataj:v:17:y:2017:i:1:p:240-249
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    Cited by:

    1. Ahmed,Tanima & Lebbos,Toni Joe & Howell,Sean & Lamontagne,Erik & Wimpey,Joshua Seth, 2022. "Impacts of COVID-19 on the Income and Mental Well-Being of Cismen, Ciswomen, Transgender, andNon-Binary Individuals : Evidence from the 2020 COVID-19 Disparities Survey," Policy Research Working Paper Series 10184, The World Bank.
    2. Deeb, Omar El, 2023. "Entropic spatial auto-correlation of voter uncertainty and voter transitions in parliamentary elections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    3. Luca Zanin, 2021. "On the estimation of Okun’s coefficient in some countries in Latin America: a comparison between OLS and GME estimators," Empirical Economics, Springer, vol. 60(3), pages 1575-1592, March.

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