USMacroG {AER}R Documentation

US Macroeconomic Data (1950–2000, Greene)

Description

Time series data on 12 US macroeconomic variables for 1950–2000.

Usage

data("USMacroG")

Format

A quarterly multiple time series from 1950(1) to 2000(4) with 12 variables.

gdp
Real gross domestic product (in billion USD),
consumption
Real consumption expenditures,
invest
Real investment by private sector,
government
Real government expenditures,
dpi
Real disposable personal income,
cpi
Consumer price index,
m1
Nominal money stock,
tbill
Quarterly average of month end 90 day treasury bill rate,
unemp
Unemployment rate,
population
Population (in million), interpolation of year end figures using constant growth rate per quarter,
inflation
Inflation rate,
interest
Ex post real interest rate (essentially, tbill - inflation).

Source

Online complements to Greene (2003). Table F5.1.

http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm

References

Greene, W.H. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.

See Also

Greene2003, USMacroSW, USMacroSWQ, USMacroSWM, USMacroB

Examples

## data and trend as used by Greene (2003)
data("USMacroG")
USMacroG <- as.ts(merge(as.zoo(USMacroG), trend = 1:nrow(USMacroG) - 1))

## Example 6.1
## Table 6.1
library("dynlm")
fm6.1 <- dynlm(log(invest) ~ tbill + inflation + log(gdp) + trend, data = USMacroG)
fm6.3 <- dynlm(log(invest) ~ I(tbill - inflation) + log(gdp) + trend, data = USMacroG)
summary(fm6.1)
summary(fm6.3)
deviance(fm6.1)
deviance(fm6.3)
vcov(fm6.1)[2,3] 

## F test
linear.hypothesis(fm6.1, "tbill + inflation = 0")
## alternatively
anova(fm6.1, fm6.3)
## t statistic
sqrt(anova(fm6.1, fm6.3)[2,5])
 
## Example 8.2
## Ct = b0 + b1*Yt + b2*Y(t-1) + v
fm1 <- dynlm(consumption ~ dpi + L(dpi), data = USMacroG)
## Ct = a0 + a1*Yt + a2*C(t-1) + u
fm2 <- dynlm(consumption ~ dpi + L(consumption), data = USMacroG)

## Cox test in both directions:
coxtest(fm1, fm2)
## ...and do the same for jtest() and encomptest().
## Notice that in this particular case two of them are coincident.
jtest(fm1, fm2)
encomptest(fm1, fm2)
## encomptest could also be performed `by hand' via
fmE <- dynlm(consumption ~ dpi + L(dpi) + L(consumption), data = USMacroG)
waldtest(fm1, fmE, fm2)

## More examples can be found in:
## help("Greene2003")

[Package AER version 1.1-2 Index]