negbinmfx: Marginal effects for a negative binomial regression.

Description

This function estimates a negative binomial regression model and calculates the corresponding marginal effects.

Usage

negbinmfx(formula, data, atmean = TRUE, robust = FALSE, clustervar1 = NULL, 
          clustervar2 = NULL, start = NULL, control = glm.control())

Arguments

formula

an object of class ``formula'' (or one that can be coerced to that class).

data

the data frame containing these data. This argument must be used.

atmean

default marginal effects represent the partial effects for the average observation. If atmean = FALSE the function calculates average partial effects.

robust

if TRUE the function reports White/robust standard errors.

clustervar1

a character value naming the first cluster on which to adjust the standard errors.

clustervar2

a character value naming the second cluster on which to adjust the standard errors for two-way clustering.

start

starting values for the parameters in the glm.nb model.

control

see glm.control.

Value

mfxest

a coefficient matrix with columns containing the estimates, associated standard errors, test statistics and p-values.

fit

the fitted glm.nb object.

dcvar

a character vector containing the variable names where the marginal effect refers to the impact of a discrete change on the outcome. For example, a factor variable.

call

the matched call.

Details

If both robust=TRUE and !is.null(clustervar1) the function overrides the robust command and computes clustered standard errors.

Examples

Run this code

# NOT RUN {
# simulate some data
set.seed(12345)
n = 1000
x = rnorm(n)
y = rnegbin(n, mu = exp(1 + 0.5 * x), theta = 0.5)

data = data.frame(y,x)

negbinmfx(formula=y~x,data=data)
# }