momentfit (version 0.5)

HealthRWM: Health data from Greene (2012) applications.

Description

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

Usage

data("HealthRWM")

Format

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

References

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.

Examples

###### 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 <- momentModel(g, h1, thet0, vcov="MDS", data=dat88)
res1 <- gmmFit(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 <- momentModel(g, h2, thet0, vcov="MDS", data=dat88)
res2 <- gmmFit(model2, type="onestep")
summary(res2, sandwich=TRUE)@coef

## Table 13.2 fourth column (Can't get closer than that)
res3 <- gmmFit(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...