Unifying regression models through maximum likelihood: gsem as a teaching tool

Isabel Canette
Additional contact information
Isabel Canette: StataCorp

Spanish Stata Conference 2026 from Stata Users Group

Abstract: Most commonly used parametric regression models share a common foundation: maximum likelihood estimation. Viewing them this way lets researchers build from simple models to complex specifications using a single statistical principle. We begin with linear regression recast as Gaussian regression, then extend it to generalized linear models for binary, count, and positive continuous outcomes. Next, we add latent variables, including hierarchical random effects, to obtain generalized multilevel models. Throughout, we illustrate each step with Stata’s gsem command for generalized structural equation modeling.

Date: 2026-10-31
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spai26:09

Access Statistics for this paper

More papers in Spanish Stata Conference 2026 from Stata Users Group Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().