sampleSelection) support a binary dependent variable in the outcome equation:
The dependent variable of of the selection equation (specified by argument selection) must have exactly two levels (e.g., 'FALSE' and 'TRUE', or '0' and '1'). By default the levels are sorted in increasing order ('FALSE' is before 'TRUE', and '0' is before '1'). If the dependent variable of the outcome equation (specified by argument outcome) has exactly two levels, this variable is modelled as a binary variable.
Nevertheless, I can't find the possibility to choose the binary model to use in the second step. In particular I would like to use a probit model instead of a linear probability model.
Example using binary variable in the outcome equation:
## We estimate the probability of womens' education on their chances to get high wage (> $5/hr in 1975 USD), using PSID data. ## We use education as explanatory variable and add age, kids, and non-work income as exclusion restrictions. library(stargazer) library(sampleSelection) data(Mroz87) ols=lm(wage >= 5 ~ educ, data=subset(Mroz87, lfp==1) ) m <- heckit(lfp ~ educ + age + kids5 + kids618 + nwifeinc, wage >= 5 ~ educ, data = Mroz87 ) stargazer(ols, m, title="Married women's wage regressions", type="text",df=FALSE, digits=4)
As you can see by the result of the code in the example, the function uses the lpm and I can't find a way to modify it.