momentfit (version 0.5)

ConsumptionG: Consumption data from Greene (2012) applications.

Description

Quarterly macroeconomic US data from 1950 to 2000.

Usage

data("ConsumptionG")

Format

A data frame with 204 observations on the following 14 variables.

YEAR

Year

QTR

Quarter

REALGDP

Read GDP

REALCONS

Real Consumption

REALINVS

Real Investment

REALGOVT

Real public expenditure

REALDPI

ector

CPI_U

CPI

M1

Money stock

TBILRATE

Interest rate

UNEMP

Unemployment rate

POP

Population

INFL

Inflation

REALINT

Real interest rate.

References

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

Examples

data(ConsumptionG)
## Get the data ready for Table 8.2 of Greene (2012)
Y <- ConsumptionG$REALDPI
C <- ConsumptionG$REALCONS
n <- nrow(ConsumptionG)
Y1 <- Y[-c(1,n)]; Y2 <- Y[-c(n-1,n)]; Y <- Y[-c(1:2)]
C1 <- C[-c(1,n)]; C <- C[-(1:2)]
dat <- data.frame(Y=Y,Y1=Y1,Y2=Y2,C=C,C1=C1)

## Starting at the NLS estimates (from the table)
theta0=c(alpha=468, beta=0.0971, gamma=1.24)

## Greene (2012) seems to assume iid errors (probably wrong assumption here)
model <- momentModel(C~alpha+beta*Y^gamma, ~C1+Y1+Y2, data=dat, theta0=theta0, vcov="iid")

### Scaling the parameters increase the speed of convergence
res <- gmmFit(model, control=list(parscale=c(1000,.1,1)))

### It also seems that there is a degree of freedom adjustment for the
### estimate of the variance of the error term.
summary(res, df.adj=TRUE)@coef