Poisson model fitting high-dimensional with k-way fixed effects

A wrapper for feglm with family = poisson().

Usage

fepoisson(
  formula = NULL,
  data = NULL,
  weights = NULL,
  beta_start = NULL,
  eta_start = 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.
beta_start: an optional vector of starting values for the structural parameters in the linear predictor. Default is \(\boldsymbol{\beta} = \mathbf{0}\).
eta_start: an optional vector of starting values for the linear predictor.
control: a named list of parameters for controlling the fitting process. See feglm_control for details.

Value

A named list of class "feglm".

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

summary(mod)
#> Formula: trade ~ log_dist + lang + cntg + clny | exp_year + imp_year
#> <environment: 0x56808865b138>
#> 
#> Family: Poisson
#> 
#> Estimates:
#> 
#> |          | Estimate | Std. Error | z value   | Pr(>|z|)   |
#> |----------|----------|------------|-----------|------------|
#> | log_dist |  -0.7937 |     0.0049 | -162.0661 | 0.0000 *** |
#> | lang     |   0.0491 |     0.0103 |    4.7808 | 0.0000 *** |
#> | cntg     |   0.6913 |     0.0113 |   61.3261 | 0.0000 *** |
#> | clny     |  -0.0239 |     0.0109 |   -2.1871 | 0.0287 *   |
#> 
#> Significance codes: *** 99.9%; ** 99%; * 95%; . 90%
#> 
#> Pseudo R-squared: 0.6274 
#> 
#> Number of observations: Full 500; Missing 0; Perfect classification 0 
#> 
#> Number of Fisher Scoring iterations: 12