*
*  Examples 17.6, 17.7
*  J. Wooldridge, Econometrics of Cross Section and Panel Data
*
*  Note: The sample data files include all required data transformations.
*        However, in many cases we show how these data transformations would
*        be created in RATS from the basic data, as this is something you will
*        need to know how to do in practice.
*
open data mroz.raw
data(format=free,org=columns) 1 753 inlf hours kidslt6 kidsge6 age educ wage repwage $
 hushrs husage huseduc huswage faminc mtr  motheduc fatheduc $
 unem city exper nwifeinc lwage expersq
*
set lwage   = log(wage)
set expersq = exper**2
*
* Estimate wage equation by OLS for those in the labor force.
*
linreg(smpl=inlf) lwage
# educ exper expersq constant
*
* As first step in "heckit" procedure, estimate a probit for the "inlf" variable,
* using the explanatory variables from the hours equation.
*
ddv(dist=probit) inlf
# constant nwifeinc educ exper expersq age kidslt6 kidsge6
*
* Compute the inverse Mills' ratios from this.
*
prj(dist=probit,mills=lambda2)
*
* Run OLS again, including lambda2 as an explanatory variable.
*
linreg(smpl=inlf) lwage
# educ exper expersq constant lambda2
*
* Same thing, but including all the model's exogenous variables.
*
linreg(smpl=inlf) lwage
# constant nwifeinc educ exper expersq age kidslt6 kidsge6 lambda2
*
*  Example 17.7
*
* Estimate labor force participation probit, replacing educ with the instruments
* selected for it. Get the inverse Mills' ratios.
*
ddv(dist=probit) inlf
# constant nwifeinc motheduc fatheduc huseduc exper expersq age kidslt6 kidsge6
prj(dist=probit,mills=lambda3)
*
* Estimate the wage equation by 2SLS, using all the exogenous variables, plus the
* inverse Mills' ratio from the selection equation.
*
instruments constant nwifeinc motheduc fatheduc huseduc exper expersq age kidslt6 kidsge6 lambda3
linreg(instruments) lwage
# educ exper expersq constant lambda3