OECDGrowth: OECD Macroeconomic Data

Description

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

Usage

data("OECDGrowth")

Arguments

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 Rechniques. Economics Letters, 71, 1--8.

Examples

Run this code

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)