AER: OECDGrowth – R documentation

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AER

OECDGrowth

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.

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)

AER

Applied Econometrics with R

v1.2-10

GPL-2 | GPL-3

Authors

Christian Kleiber [aut] (<https://orcid.org/0000-0002-6781-4733>), Achim Zeileis [aut, cre] (<https://orcid.org/0000-0003-0918-3766>)

Initial release

2022-06-13