Implementing factor models for unobserved heterogeneity in Stata

Miguel Sarzosa and Sergio Urzua

Stata Journal, 2016, vol. 16, issue 1, 197-228

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.

Keywords: heterofactor; unobserved heterogeneity; factor models; Roy model; maximum likelihood; numerical integration (search for similar items in EconPapers)
Date: 2016
Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj16-1/st0431/
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0431 link to article purchase

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:16:y:2016:i:1:p:197-228

Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html

Access Statistics for this article

Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins

More articles in Stata Journal from StataCorp LLC
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().