Negative Binomial model fitting with high-dimensional k-way fixed effects

A routine that uses the same internals as feglm.

Usage

fenegbin(
  formula = NULL,
  data = NULL,
  weights = NULL,
  beta_start = NULL,
  eta_start = NULL,
  init_theta = NULL,
  link = c("log", "identity", "sqrt"),
  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.
init_theta: an optional initial value for the theta parameter (see glm.nb).
link: the link function. Must be one of "log", "sqrt", or "identity".
control: a named list of parameters for controlling the fitting process. See feglm_control for details.

Value

A named list of class "feglm". The list contains the following eighteen elements:

coefficients: a named vector of the estimated coefficients
eta: a vector of the linear predictor
weights: a vector of the weights used in the estimation
hessian: a matrix with the numerical second derivatives
deviance: the deviance of the model
null_deviance: the null deviance of the model
conv: a logical indicating whether the model converged
iter: the number of iterations needed to converge
theta: the estimated theta parameter
iter.outer: the number of outer iterations
conv.outer: a logical indicating whether the outer loop converged
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 effects
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
family: the family used in the model
control: the control list used in the model

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), 700), ]

mod <- fenegbin(
  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: 0x568089444240>
#> 
#> Family: Negative Binomial(1.9067)
#> 
#> Estimates:
#> 
#> |          | Estimate | Std. Error | z value  | Pr(>|z|)   |
#> |----------|----------|------------|----------|------------|
#> | log_dist |  -1.1863 |     0.0651 | -18.2286 | 0.0000 *** |
#> | lang     |   0.6575 |     0.1208 |   5.4413 | 0.0000 *** |
#> | cntg     |   0.4718 |     0.2304 |   2.0477 | 0.0406 *   |
#> | clny     |   0.5040 |     0.2261 |   2.2293 | 0.0258 *   |
#> 
#> Significance codes: *** 99.9%; ** 99%; * 95%; . 90%
#> 
#> Number of observations: Full 700; Missing 0; Perfect classification 0 
#> 
#> Number of Fisher Scoring iterations: 1
#> Number of outer iterations: 4
#> theta= 1.907, std. error= 0.115