heckitrob: Robust Heckit Fit
In ssmrob: Robust Estimation and Inference in Sample Selection Models

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

Fits the sample selection model using a robust two-stage estimator

1	heckitrob(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 `heckitrob` is called.
`control`	a list of parameters for controlling the fitting process

Compute robust two-stage estimates of the Heckman's selection model. The robust probit is fitted in the first stage. In the second stage the Mallows type M-estimator is used. The values of the tuning constants and the robustness weights can be modified in heckitrob.control.

Object of class "heckitrob".

`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
`IMR1`	inverse Mills ratio for the case when y_1=1
`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

Mikhail Zhelonkin, Marc G. Genton, Elvezio Ronchetti

Cameron, C.A., Trivedi, P.K. (2009) Microeconometrics Using Stata. College Station, TX: Stata Press.

Heckman, J.J. (1979) Sample Selection Bias as a Specification Error. Econometrica, 47, p. 153-161.

Zhelonkin, M., Genton M.G., and Ronchetti, E. (2016) Robust Inference in Sample Selection Models. Journal of the Royal Statistical Society, Series B, 78, p. 805-827. doi: 10.1111/rssb.12136

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, heckit5rob, etregrob

# Zhelonkin, Genton, Ronchetti (2016): page 823
data(MEPS2001)
selectEq <- dambexp ~ age + female + educ + blhisp + totchr + ins
outcomeEq <- lnambx ~ age + female + educ + blhisp + totchr + ins
meps.fit <- ssmrob(selectEq, outcomeEq, data = MEPS2001, control = heckitrob.control(tcc = 3.2))
summary(meps.fit)

Call: 
ssmrob(selection = selectEq, outcome = outcomeEq, data = MEPS2001, 
    control = heckitrob.control(tcc = 3.2))
 
Heckman selection model / robust 2-step M-estimation 
3328 observations: 526 censored and 2802 observed 
Probit selection equation: 
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.74914    0.19507  -3.840 1.23e-04 ***
age          0.10541    0.02770   3.806 1.41e-04 ***
femaleTRUE   0.68741    0.06226  11.040 2.41e-28 ***
educ         0.07012    0.01147   6.116 9.62e-10 ***
blhispTRUE  -0.39775    0.06265  -6.349 2.17e-10 ***
totchr       0.83284    0.08028  10.370 3.24e-25 ***
insTRUE      0.18256    0.06371   2.865 4.17e-03 ** 
Outcome equation: 
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  5.40154    0.27673  19.520 7.53e-85 ***
age          0.20062    0.02451   8.186 2.70e-16 ***
femaleTRUE   0.25501    0.06993   3.647 2.66e-04 ***
educ         0.01325    0.01162   1.141 2.54e-01    
blhispTRUE  -0.15508    0.06507  -2.383 1.72e-02 *  
totchr       0.48116    0.03823  12.590 2.52e-36 ***
insTRUE     -0.06707    0.05159  -1.300 1.94e-01    
IMR1        -0.67676    0.25928  -2.610 9.05e-03 ** 
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 
 
sigma  1.317891