Estimation of multiprocess survival models with cmp
Tamás Bartus and
David Roodman
Stata Journal, 2014, vol. 14, issue 4, 756-777
Abstract:
Multilevel multiprocess hazard models are routinely used by demographers to control for endogeneity and selection effects. These models consist of multilevel proportional hazards equations, and possibly probit equations, with correlated random effects. Although Stata currently lacks a specialized command for fitting systems of multilevel proportional hazards models, systems of seemingly unrelated lognormal survival models can be fit with the user-written cmp command (Roodman 2011, Stata Journal 11: 159–206). In this article, we describe multiprocess survival models and demonstrate theoretical and practical aspects of estimation. We also illustrate the application of the cmp command using examples related to demographic research. The examples use a dataset shipped with the statistical software aML. Copyright 2014 by StataCorp LP.
Keywords: survival analysis; multilevel analysis; multilevel multiprocess hazard model; simultaneous equations; SUR estimation; cmp (search for similar items in EconPapers)
Date: 2014
Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj14-4/st0358/
References: Add references at CitEc
Citations: View citations in EconPapers (11)
Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0358 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:14:y:2014:i:4:p:756-777
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 ().