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Implementing factor models for unobserved heterogeneity in Stata

Author

Listed:
  • Miguel Sarzosa

    (Purdue University)

  • Sergio Urzúa

    (University of Maryland)

Abstract

We introduce a new command, heterofactor, for the maximum likeli- hood estimation of models with unobserved heterogeneity, including a Roy model. heterofactor fits models with up to four latent factors and allows the unobserved heterogeneity to follow general distributions. Our command differs from Stata’s sem command in that it does not rely on the linearity of the structural equations and distributional assumptions for identification of the unobserved heterogeneity. It uses the estimated distributions to numerically integrate over the unobserved factors in the outcome equations by using a mixture of normals in a Gauss–Hermite quadrature. heterofactor delivers consistent estimates, including the unobserved factor loadings, in a variety of model structures. Copyright 2016 by StataCorp LP.

Suggested Citation

  • Miguel Sarzosa & Sergio Urzúa, 2016. "Implementing factor models for unobserved heterogeneity in Stata," Stata Journal, StataCorp LP, vol. 16(1), pages 197-228, March.
    • Handle: RePEc:tsj:stataj:v:16:y:2016:i:1:p:197-228
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    Cited by:

    1. Jiang, Xuan, 2018. "Planting the Seeds for Success: Why Women in STEM Do Not Stick in the Field," MPRA Paper 89650, University Library of Munich, Germany.
    2. Armin Falk & Fabian Kosse & Pia Pinger & Hannah Schildberg-Hörisch & Thomas Deckers, 2021. "Socioeconomic Status and Inequalities in Children’s IQ and Economic Preferences," Journal of Political Economy, University of Chicago Press, vol. 129(9), pages 2504-2545.
    3. Jiang, Xuan, 2021. "Women in STEM: Ability, preference, and value," Labour Economics, Elsevier, vol. 70(C).
    4. Sarzosa, Miguel, 2023. "Sexual Orientation and Labor Market Disparities," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 723-755.

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