feglm.nb: Efficiently fit negative binomial glm's with high-dimensional \(k\)-way fixed effects

Description

feglm.nb can be used to fit negative binomial generalized linear models with many high-dimensional fixed effects (see feglm).

Usage

feglm.nb(
  formula = NULL,
  data = NULL,
  weights = NULL,
  beta.start = NULL,
  eta.start = NULL,
  init.theta = NULL,
  link = c("log", "identity", "sqrt"),
  control = NULL
)

Value

The function feglm.nb returns a named list of class "feglm".

Arguments

formula, data, weights, beta.start, eta.start, control: see feglm.
init.theta: an optional initial value for the theta parameter (see glm.nb).
link: the link function. Must be one of "log", "sqrt", or "identity".

Details

If feglm.nb does not converge this is usually a sign of linear dependence between one or more regressors and a fixed effects category. In this case, you should carefully inspect your model specification.

References

Gaure, S. (2013). "OLS with Multiple High Dimensional Category Variables". Computational Statistics and Data Analysis. 66.

Marschner, I. (2011). "glm2: Fitting generalized linear models with convergence problems". The R Journal, 3(2).

Stammann, A., F. Heiss, and D. McFadden (2016). "Estimating Fixed Effects Logit Models with Large Panel Data". Working paper.