fenegbin function

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

A routine that uses the same internals as feglm.

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 $\beta = 0$.

• 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.

Returns

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)