probitmfx {mfx}

Marginal effects for a probit regression.

Description

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

Usage

probitmfx(formula, data, atmean = TRUE, robust = FALSE, clustervar1 = NULL, 
          clustervar2 = NULL, start = NULL, control = list())

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 model.

control

see glm.control.

Details

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

Value

mfxest

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

fit

the fitted glm 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.

References

William H. Greene (2008). Econometric Analysis (6th ed.). Prentice Hall, N.Y. pp 770-787.

See Also

glm

Examples

# simulate some data
set.seed(12345)
n = 1000
x = rnorm(n)

# binary outcome
y = ifelse(pnorm(1 + 0.5*x + rnorm(n))>0.5, 1, 0)

data = data.frame(y,x)
probitmfx(formula=y~x, data=data)