HealthRWM: Health data from Greene (2012) applications.
In gmm4: S4 Generalized Method of Moments

Description Usage Format Source References Examples

The dataset is used in Greene (2012) and is taken from Riphahn, Wambach, Million (2003).

1	data("HealthRWM")

A data frame with 27326 observations on the following 25 variables.

ID: Person-identification number
female: Female=1; male=0
year: Calendar year of the observation
age: Age in years
hsat: Health satisfaction, coded 0 (low) to 10 (high)
handdum: Handicapped=1; otherwise=0
handper: Degree of handicap in percent (0 to 100)
hhninc: Household nominal monthly net income in German marks/10,000
hhkids: Children under age 16 in the household=1; otherwise=0
educ: Years of schooling
married: Married=1; otherwise=0
haupts: Highest schooling degree is Hauptschul degree=1; otherwise=0
reals: Highest schooling degree is Realschul degree=1; otherwise=0
fachhs: Highest schooling degree is Polytechnical degree=1; otherwise=0
abitur: Highest schooling degree is Abitur=1; otherwise=0
univ: Highest schooling degree is university degree=1; otherwise=0
working: Employed=1; otherwise=0
bluec: Blue-collar employee=1; otherwise=0
whitec: White-collar employee=1; otherwise=0
self: Self-employed=1; otherwise=0
beamt: Civil servant=1; otherwise=0
docvis: Number of doctor visits in last three months,
hospvis: Number of hospital visits in last calendar year,
public: Insured in public health insurance=1; otherwise=0
addon: Insured by add-on insurance=1; otherwise=0

On Greene (2012) online resources, and on the Journal of Applied Econometrics website (http://qed.econ.queensu.ca/jae/2003-v18.4/riphahn-wambach-million/).

Riphahn, R.T. and Wambach, A. and Million, A. (2003), Incentive Effects in the Demand for Health Care: A Bivariate Panel Count Data Estimation, Journal of Applied Econometrics, Vol. 18, No. 4, 387–405.

Green, W.H.. (2012). Econometric Analysis, 7th edition, Prentice Hall.

###### Example 13.7 of Greene (2012)
####################################

## Selecting the same data point and scaling income
##########
data(HealthRWM)
dat88 <- subset(HealthRWM, year==1988 & hhninc>0)
dat88$hhninc <- dat88$hhninc/10000

### A guess start
thet0 <- c(b0=log(mean(dat88$hhninc)),b1=0,b2=0,b3=0)

## Table 13.2 First column
g <- hhninc~exp(b0+b1*age+b2*educ+b3*female)
res0 <- nls(g, dat88, start=thet0, control=list(maxiter=100))
summary(res0)$coef

## Table 13.2 Second column
## Trying very hard to reproduce the results, 
## Who is right?
h1 <- ~age+educ+female
model1 <- gmmModel(g, h1, thet0, vcov="MDS", data=dat88)
res1 <- modelFit(model1, control=list(reltol=1e-10, abstol=1e-10))
summary(res1)@coef

## Table 13.2 third column (close enough)
## Here a sandwich vcov is required because it is not
## efficient GMM
h2 <- ~age+educ+female+hsat+married
model2 <- gmmModel(g, h2, thet0, vcov="MDS", data=dat88)
res2 <- modelFit(model2, type="onestep")
summary(res2, sandwich=TRUE)@coef

## Table 13.2 fourth column (Can't get closer than that)
res3 <- modelFit(model2)
summary(res3)@coef

# Lets see what happens if we start on Greene solution
             
update(res3, theta0=c(b0=-1.61192, b1=.00092, b2=.04647, b3=-.01517))

## No...