felm: LM fitting with high-dimensional k-way fixed effects

In capybara: Fast and Memory Efficient Fitting of Linear Models with High-Dimensional Fixed Effects

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 "formula": a symbolic description of the model to be fitted. formula must be of type y ~ x | k, where the second part of the formula refers to factors to be concentrated out. It is also possible to pass clustering variables to feglm as y ~ x | k | c.
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 feglm_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
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

# subset trade flows to avoid fitting time warnings during check
set.seed(123)
trade_2006 <- trade_panel[trade_panel$year == 2006, ]
trade_2006 <- trade_2006[sample(nrow(trade_2006), 500), ]

mod <- felm(
  log(trade) ~ log_dist + lang + cntg + clny | exp_year + imp_year,
  trade_2006
)

summary(mod)

capybara documentation built on April 11, 2025, 5:41 p.m.