| felm {capybara} | R Documentation |
LM fitting with high-dimensional k-way fixed effects
Description
feglm can be used to fit linear models with many high-dimensional fixed effects. The estimation procedure is based on unconditional maximum likelihood and can be interpreted as a “weighted demeaning” approach.
Usage
felm(formula = NULL, data = NULL, weights = NULL, control = NULL)
Arguments
formula |
an object of class "formula": a symbolic description of the model to be fitted. formula
must be of type response ~ slopes | fixed_effects | cluster.
|
data |
an object of class "data.frame" containing the variables in the model. The expected input is a
dataset with the variables specified in formula and a number of rows at least equal to the number of variables
in the model.
|
weights |
an optional string with the name of the prior weights variable in data.
|
control |
a named list of parameters for controlling the fitting process. See fit_control for details. |
Value
A named list of class "felm". The list contains the following
eleven elements:
coefficients |
a named vector of the estimated coefficients |
fitted_values |
a vector of the estimated dependent variable |
weights |
a vector of the weights used in the estimation |
hessian |
a matrix with the numerical second derivatives |
null_deviance |
the null deviance of the model |
nobs |
a named vector with the number of observations used in the estimation indicating the dropped and perfectly predicted observations |
fe_levels |
a named vector with the number of levels in each fixed effect |
nms_fe |
a list with the names of the fixed effects variables |
formula |
the formula used in the model |
data |
the data used in the model after dropping non-contributing observations |
control |
the control list used in the model |
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.
Stammann, A. (2018). "Fast and Feasible Estimation of Generalized Linear Models with High-Dimensional k-Way Fixed Effects". ArXiv e-prints.
Examples
# Model with fixed effects
mod <- felm(log(mpg) ~ log(wt) | cyl, mtcars)
summary(mod)
# Model without fixed effects but with clustered standard errors
# Note: Use 0 to indicate no fixed effects when specifying clusters
mod <- felm(log(mpg) ~ log(wt) | 0 | cyl, mtcars)
summary(mod)