R: US Macroeconomic Data (1950-2000, Greene)

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")
    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")

[Package AER version 1.2-5 Index]