capybara (version 0.9.3)

felm: 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)

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

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.

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)