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.
https://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")
ltrend <- 1:nrow(USMacroG) - 1
## Example 6.1
## Table 6.1
library("dynlm")
fm6.1 <- dynlm(log(invest) ~ tbill + inflation + log(gdp) + ltrend, data = USMacroG)
fm6.3 <- dynlm(log(invest) ~ I(tbill - inflation) + log(gdp) + ltrend, data = USMacroG)
summary(fm6.1)
summary(fm6.3)
deviance(fm6.1)
deviance(fm6.3)
vcov(fm6.1)[2,3]
## F test
linearHypothesis(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")