heckitrob: Robust Heckit Fit

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

View source: R/heckitrob.R

Description

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

Usage

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

Arguments

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

Details

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.

Value

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

Author(s)

Mikhail Zhelonkin, Marc G. Genton, Elvezio Ronchetti

References

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

See Also

glmrob, rlm, ssmrob, heckitrob.control, heckit5rob, etregrob

Examples

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# 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)

Example output

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  

ssmrob documentation built on April 2, 2020, 5:07 p.m.