felm {capybara} | R Documentation |
LM fitting with high-dimensional k-way fixed effects
Description
A wrapper for feglm
with
family = gaussian()
.
Usage
felm(formula = NULL, data = NULL, weights = NULL, control = NULL)
Arguments
formula |
an object of class |
data |
an object of class |
weights |
an optional string with the name of the 'prior weights'
variable in |
control |
a named list of parameters for controlling the fitting
process. See |
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 |
lvls_k |
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
# check the feglm examples for the details about clustered standard errors
mod <- felm(log(mpg) ~ log(wt) | cyl, mtcars)
summary(mod)