ConsumptionG: Consumption data from Greene (2012) applications.

In gmm4: S4 Generalized Method of Moments

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.

Source

Greene (2012) online resources: (http://pages.stern.nyu.edu/~wgreene/Text/Edition7/tablelist8new.htm)

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 <- gmmModel(C~alpha+beta*Y^gamma, ~C1+Y1+Y2, data=dat, theta0=theta0, vcov="iid")

### Scaling the parameters increase the speed of convergence
res <- modelFit(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

gmm4 documentation built on Dec. 6, 2019, 3:01 a.m.