*
*  Example 5.3 and 5.4
*  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) 1 428 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 reduced form for educ and test exclusion of the potential instruments.
*
linreg educ
# constant exper expersq motheduc fatheduc huseduc
exclude
# motheduc fatheduc huseduc
*
*  Run 2SLS - list all instruments, including the constant
*
instruments constant exper expersq motheduc fatheduc huseduc
linreg(instruments) lwage
# constant exper expersq educ
*
*  Example 5.4, add the kids variables to the instruments and
*  the regressors
*
instruments constant exper expersq motheduc fatheduc huseduc kidslt6 kidsge6
linreg(instruments) lwage
# constant exper expersq educ kidslt6 kidsge6
*
*  Test the two kids variables using a Wald test
*
exclude
# kidslt6 kidsge6
*
* LM version described at the bottom of page 99. Compute the restricted 2SLS
* regression and save the residuals. Because the variables being tested are in
* the instrument set, the fitted values are the variables themselves.
*
linreg(instruments) lwage / resids
# constant exper expersq educ
linreg resids
# kidslt6 kidsge6
cdf chisqr %trsq 2