LM fitting with high-dimensional k-way fixed effects

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)
#> Formula: log(trade) ~ log_dist + lang + cntg + clny | exp_year + imp_year
#> <environment: 0x568089fc78a8>
#> 
#> Estimates:
#> 
#> |          | Estimate   | Std. Error | z value     | Pr(>|z|)   |
#> |----------|------------|------------|-------------|------------|
#> | log_dist | -1147.0493 |     0.1035 | -11080.2479 | 0.0000 *** |
#> | lang     |   444.9385 |     0.1866 |   2384.1118 | 0.0000 *** |
#> | cntg     | 14533.5766 |     0.3896 |  37304.2094 | 0.0000 *** |
#> | clny     | -4664.8192 |     0.3761 | -12403.7924 | 0.0000 *** |
#> 
#> Significance codes: *** 99.9%; ** 99%; * 95%; . 90%
#> 
#> R-squared     : 0.6459 
#> Adj. R-squared: 0.5078 
#> 
#> Number of observations: Full 500; Missing 0; Perfect classification 0