Robust Sample Selection Model

Description

Function provides the robust two-stage estimators of truncated selection model (Tobit-2) and switching regression model (Tobit-5).

Usage

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

Arguments

outcome

formula(s), the outcome equation(s)

selection

formula, the selection equation

control

a list of parameters for controlling the fitting process

Details

Outcome equation may be a simple formula for the case of truncated selection model, or a list of two formulas for the case of switching regressions.

Value

Object of class "heckitrob" or object of class "heckit5rob".

Author(s)

Mikhail Zhelonkin, Marc G. Genton, Elvezio Ronchetti

References

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

Zhelonkin, M., Genton M.G., and Ronchetti, E. (2013). Robust Inference in Sample Selection Models, Manuscript.

Amemiya, T. (1984). Tobit Models: a Survey. Journal of Econometrics, 24, p. 3-61.

See Also

heckitrob, heckit5rob

Examples

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# sample selection model (Tobit-2)
data(MEPS2001)
attach(MEPS2001)
selectEq <- dambexp ~ age+female+educ+blhisp+totchr+ins
outcomeEq <- lnambx ~ age+female+educ+blhisp+totchr+ins
summary(ssmrob(outcomeEq,selectEq,control=heckitrob.control(tcc=3.2,weights.x1="robCov")))


# switching regressions example (Tobit-5)
library(mvtnorm)
covm <- diag(3)
covm[lower.tri(covm)] <- c(0.75, 0.5, 0.25)
covm[upper.tri(covm)] <- covm[lower.tri(covm)]
eps <- rmvnorm(1000, rep(0, 3), covm)
x1 <- rnorm(1000)
y1 <- x1 + eps[,1] > 0
x21 <- rnorm(1000)
x22 <- rnorm(1000)
y2=ifelse(y1 > 0.5, x21 + eps[,2], x22 + eps[,3])
summary(ssmrob(list(y2 ~ x21, y2 ~ x22), y1 ~ x1))

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