Estimation of Competing Risks Duration Models with Unobserved Heterogeneity Using hsmlogit

Abstract

This article presents hsmlogit, a new Stata command that estimates multispells discrete time competing risks duration models with unobserved heterogeneity. hsmlogit allows for the estimation of one, two and up to three competing risks, as well as a maximum of ve points of support for the identication of unobserved heterogeneity distribution (Heckman and Singer, 1984). The main contribution of hsmlogit is that allows for exploiting the richness of large longitudinal micro datasets, by estimating competing risks duration models, instead of one-risk models (such as hshaz and hshaz2), as well as it takes into account the presence of unobserved heterogeneity affecting transition rates. In addition to this, and taking into account the larger size of longitudinal micro datasets used for the estimation of discrete time duration models, hsmlogit also provides the algebraic expressions of both rst and second order derivatives that, respectively, dene the gradient vector and Hessian matrix, which signicantly reduce time required to achieve model convergence.