R: OECD Macroeconomic Data

OECDGrowth {AER} | R Documentation

## OECD Macroeconomic Data

### Description

Cross-section data on OECD countries, used for growth regressions.

### Usage

    data("OECDGrowth")

### Format

A data frame with 22 observations on the following 6 variables.

gdp85

  real GDP in 1985 (per person of working age, i.e., age 15 to 65), in 1985 international prices.

gdp60

  real GDP in 1960 (per person of working age, i.e., age 15 to 65), in 1985 international prices.

invest

  average of annual ratios of real domestic investment to real GDP (1960–1985).

school

  percentage of the working-age population that is in secondary school.

randd

  average of annual ratios of gross domestic expenditure on research and development to nominal GDP (of available observations during 1960–1985).

popgrowth

  annual population growth 1960–1985, computed as `log(pop85/pop60)/25`.

### Source

Appendix 1 Nonneman and Vanhoudt (1996), except for one bad misprint: The value of `school` for Norway is given as 0.01, the correct value is 0.1 (see Mankiw, Romer and Weil, 1992). `OECDGrowth` contains the corrected data.

### References

Mankiw, N.G., Romer, D., and Weil, D.N. (1992). A Contribution to the Empirics of Economic Growth. Quarterly Journal of Economics, 107, 407–437.

Nonneman, W., and Vanhoudt, P. (1996). A Further Augmentation of the Solow Model and the Empirics of Economic Growth. Quarterly Journal of Economics, 111, 943–953.

Zaman, A., Rousseeuw, P.J., and Orhan, M. (2001). Econometric Applications of High-Breakdown Robust Regression Techniques. Economics Letters, 71, 1–8.

### See Also

`GrowthDJ`, `GrowthSW`

### Examples

    data("OECDGrowth")

    ## Nonneman and Vanhoudt (1996), Table II
    cor(OECDGrowth[, 3:6])
    cor(log(OECDGrowth[, 3:6]))

    ## textbook Solow model
    ## Nonneman and Vanhoudt (1996), Table IV, and
    ## Zaman, Rousseeuw and Orhan (2001), Table 2
    so_ols <- lm(log(gdp85/gdp60) ~ log(gdp60) + log(invest) + log(popgrowth+.05),
      data = OECDGrowth)
    summary(so_ols)

    ## augmented and extended Solow growth model
    ## Nonneman and Vanhoudt (1996), Table IV
    aso_ols <- lm(log(gdp85/gdp60) ~ log(gdp60) + log(invest) +
      log(school) + log(popgrowth+.05), data = OECDGrowth)
    eso_ols <- lm(log(gdp85/gdp60) ~ log(gdp60) + log(invest) +
      log(school) + log(randd) + log(popgrowth+.05), data = OECDGrowth)

    ## determine unusual observations using LTS
    library("MASS")
    so_lts <- lqs(log(gdp85/gdp60) ~ log(gdp60) +  log(invest) + log(popgrowth+.05),
      data = OECDGrowth, psamp = 13, nsamp = "exact")

    ## large residuals
    nok1 <- abs(residuals(so_lts))/so_lts$scale[2] > 2.5
    residuals(so_lts)[nok1]/so_lts$scale[2]

    ## high leverage
    X <- model.matrix(so_ols)[,-1]
    cv <- cov.rob(X, nsamp = "exact")
    mh <- sqrt(mahalanobis(X, cv$center, cv$cov))
    nok2 <- mh > 2.5
    mh[nok2]

    ## bad leverage
    nok <- which(nok1 & nok2)
    nok

    ## robust results without bad leverage points
    so_rob <- update(so_ols, subset = -nok)
    summary(so_rob)
    ## This is similar to Zaman, Rousseeuw and Orhan (2001), Table 2
    ## but uses exact computations (and not sub-optimal results
    ## for the robust functions lqs and cov.rob)

[Package AER version 1.2-5 Index]