etregrob: Robust Fit of Endogenous Treatment Model
In ssmrob: Robust Estimation and Inference in Sample Selection Models

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Fits the endogenous treatment model using a robust two-stage estimator

1	etregrob(selection, outcome, data, control = heckitrob.control())

`selection`	formula, the selection equation
`outcome`	formula, the outcome equation
`data`	an optional data frame containing the variables in the model. If not found in data, the variables are taken from `environment(formula)`, typically the environment from which `etregrob` is called.
`control`	a list of parameters for controlling the fitting process. The same list as for sample selection model

Compute robust two-step estimates of the Endogenous Treatment Model. The robust probit is fitted in the first stage. In the second stage the Mallows type M-estimator is used instead of traditional OLS. The correction for endogeneity is made by means of control function, which is the inverse Mills ratio for a complete sample (see Maddala, 1983, p. 120-122). The values of the tuning constants and the robustness weights can be modified in heckitrob.control.

Object of class "etregrob".

`coefficients`	a named vector of coefficients
`stage1`	object of class `glmrob` that contains robust probit fit
`stage2`	object of class `rlm` that contains second stage robust fit. Note that the standard errors in this object are biased, since they are not corrected for the uncertainty in the first estimation step. Use `vcov` below
`vcov`	variance matrix of the second stage
`sigma`	the standard error of the error term of the outcome equation
`CIMR`	inverse Mills ratio for the complete sample
`call`	the matched call
`method`	method of estimation, currently only "robust two-stage" is implemented
`converged`	logical. Did all the estimators converge?
`iterations`	list containing the numbers of iterations

The treatment variable is automatically included in the formula for the second estimation step, i.e. one should not add the dependent variable from the selection equation in the formula of the outcome equation.

Mikhail Zhelonkin

Maddala G.S. (1983) Limited-Dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press.

Zhelonkin, M., Ronchetti, E. (2021) Robust Analysis of Sample Selection Models through the R Package ssmrob. Journal of Statistical Software, 99, 4, p. 1-35. doi: 10.18637/jss.v099.i04

glmrob, rlm, ssmrob, heckitrob.control

library(mvtnorm)
set.seed(2)
N <- 3000
beta1 <- c(1.0, 1.0, 0.75)
beta2 <- c(1.5, 1.0, 0.5)
alpha <- 1.25
x1 <- rmvnorm(N, mean = c(0, -1, 1), sigma = diag(c(1, 0.5, 1)))
x2 <- x1 
x2[, 3] <- rnorm(N, 1, 1)
eps <- rmvnorm(N, mean = rep(0, 2), sigma = matrix(c(1, -0.7, -0.7, 1), 2, 2))
x1beta1 <- x1[, 1]*beta1[1] + x1[, 2]*beta1[2] + x1[, 3]*beta1[3]
x2beta2 <- x2[, 1]*beta2[1] + x2[, 2]*beta2[2] + x2[, 3]*beta2[3]
y1 <- ifelse(x1beta1 + eps[, 1] > 0, 1, 0)
y2 <- x2beta2 + alpha*y1 + eps[,2]
etm.ctrl <- heckitrob.control(weights.x1 = "hat", weights.x2 = "covMcd")
etmsim.fit <- etregrob(y1 ~ x1, y2 ~ x2, control = etm.ctrl)
summary(etmsim.fit)