Estimating Treatment Effects for Ordered Outcomes Using Maximum Simulated Likelihood

Christian Gregory ()
Additional contact information
Christian Gregory: Economic Research Service, USDA

2015 Stata Conference from Stata Users Group

Abstract: This presentation introduces 4 new modules: -treatoprobit-, -switchoprobit-, -treatoprobitsim-, and -switchoprobitsim-. Each of these routines estimates a model in which a binary endogeneous variable is affects an ordered outcome. -treatoprobit- and -switchoprobit- estimate treatment and outcome under the assumption that the error terms in the selection and outcome process are distributed as bivariate normal. -treatoprobitsim- and -switchoprobitsim- allow researchers to relax this assumption by estimating models in which a latent factor with a potentially non-normal distribution accounts for the correlation between treatment and outcome. -treatoprobit- and -treatoprobitsim- operate under the assumption of a single outcome regime for treated and untreated groups; -switchoprobit- and -switchoprobitsim- work under (and test) the assumption that outcome processes for treated and untreated ought to be handled as distinct. The presentation will introduce the modules, show Monte Carlo evidence regarding their performance, and offer an example of their use. This presentation is based on an article that is currently under review at the Stata Journal.

Date: 2015-08-02
References: Add references at CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://repec.org/col2015/columbus15_gregory.pdf

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:boc:scon15:2

Access Statistics for this paper

More papers in 2015 Stata Conference from Stata Users Group Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().