Implementing factor models for unobserved heterogeneity in Stata
Miguel Sarzosa
Purdue University
West Lafayette, IN
[email protected]
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Sergio Urzúa
University of Maryland
College Park, MD
and National Bureau of Economic Research
Cambridge, MA
[email protected]
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Abstract. We introduce a new command, heterofactor, for the maximum likelihood
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.
View all articles by these authors:
Miguel Sarzosa, Sergio Urzúa
View all articles with these keywords:
heterofactor, unobserved heterogeneity, factor models, Roy model, maximum likelihood, numerical integration
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