# ConsumptionG: Consumption data from Greene (2012) applications. In gmm4: S4 Generalized Method of Moments

## Description

Quarterly macroeconomic US data from 1950 to 2000.

## Usage

 `1` ```data("ConsumptionG") ```

## Format

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

`YEAR`

Year

`QTR`

Quarter

`REALGDP`

`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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```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.