Description

Partials out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is restricted to glm's that are based on maximum likelihood estimation. This excludes all quasi-variants of glm. The package also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models (logit and probit) derived by Fernández-Val and Weidner (2016) and Hinz, Stammann, and Wanner (2020).

Note: Linear models are also beyond the scope of this package since there is already a comprehensive procedure available felm.