| Mroz | R Documentation |
The dataset was used by Mroz (1987) and in examples in Wooldridge (2016)
data("Mroz")
A data frame with 753 observations on the following 22 variables.
inlf=1 if in lab frce, 1975
hourshours worked, 1975
kidslt6number of kids < 6 years
kidsge6number of kids 6-18
agewoman's age in years
educyears of schooling
wageEstimated wage from earnings and hours
repwagereported wage at interview in 1976
hushrshours worked by husband, 1975
husagehusband's age
huseduchusband's years of schooling
huswagehusband's hourly wage, 1975
famincfamily income, 1975
mtrfederal marginal tax rate facing woman
motheducmother's years of schooling
fatheducfather's years of schooling
unemunemployment rate in county of residence
city=1 if live in SMSA
experactual labor market experience
nwifeinc(faminc - wage*hours)/1000
From Wooldridge (2016) online resources.
Mroz, T.A. (1987), The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions, Econometrica, 55, 657–678. 387–405.
Wooldridge, J.M. (2016). Introductory Econometrics, A Modern Approach, 6th edition, Cengage Learning.
## Example 15.1 of Wooldridge (2016)
data(Mroz)
Mroz <- subset(Mroz, hours>0)
## I guess IID is assumed (That's how we get the same s.e.)
## By default a sandwich vcov is computed because it is
## a just-identified model.
res4 <- gmm4(log(wage)~educ, ~fatheduc, vcov="iid", data=Mroz)
summary(res4)
## If we adjust the variance of the residuals, however,
## we are a little off (very little)
summary(res4, df.adj=TRUE)
## Example 15.5 of Wooldridge (2016)
## Need to adjust for degrees of freedom in order
## to get the same s.e.
## The first stage F-test is very different though
## Cannot get the same even if do it manually
## with the linearHypothesis from the car package
model <- momentModel(log(wage)~educ+exper+I(exper^2),
~exper+I(exper^2)+fatheduc+motheduc, vcov="iid", data=Mroz)
res <- tsls(model)
summary(res, df.adj=TRUE)
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