Within- and Between-cluster Effects in Generalized Linear Mixed Models: A Discussion of Approaches and the Xthybrid command
By R. Schunck, Francisco Perales. 2017
202 citations recorded by the source corpus.
One typically analyzes clustered data using random- or fixed-effects models. Fixed-effects models allow consistent estimation of the effects of level-one variables, even if there is unobserved heterogeneity at level two. However, these models cannot estimate the effects of level-two variables. Hybrid and correlated random-effects models are flexible modeling specifications that separate within-and between-cluster effects and allow for both consistent estimation of level-one effects and inclusion of level-two variables. In this article, we elaborate on the separation of within- and between-cluster effects in generalized linear mixed models. These models present a unifying framework for an entire class of models whose response variables follow a distribution from the exponential family (for example, linear, logit, probit, ordered probit and logit, Poisson, and negative binomial models). We introduce the user-written command xthybrid, a shell for the meglm command. xthybrid can fit a variety of hybrid and correlated random-effects models.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/2a45c1f733ff301bd872d5f1fe088c11092ecb40
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Citations in the corpus
- Enhancing Rigor in Quantitative Entrepreneurship Research (2019, methodology): “A hybrid model that explicitly separates the between- and within- unit effects is often a preferable approach (Certo et al., 2017; Dieleman & Templin, 2014; Mundlak, 1978; Schunck & Perales, 2017).”