GrowthMPP {WALS} | R Documentation |
Growth regression data used in Magnus et al. (2010).
GrowthMPP
A data frame with 72 observations on 11 variables:
factor. Name of the country.
Average growth rate of GDP per capita from 1960 - 1996 at purchasing power parity.
Logarithm of GDP per capita in 1960.
Average real equipment investment share of GDP from 1960 - 1985 comprising investments in electrical and nonelectrical machinery (in relative prices constant across countries).
Enrollment rate for primary education in 1960.
Life expectancy at age 0 in 1960.
Average growth rate of population from 1960 - 1996.
Index for the overall maintenance of the rule of law ('law and order tradition').
Proportion of country's land area within geographical tropics.
Average of five different indices of ethnolinguistic fragmentation which is measured as the probability of two random people in a country not sharing the same language.
Fraction of Confucian population in 1970 and 1980.
The dataset is used in Magnus et al. (2010) to illustrate the WALS model averaging approach and combines the data used in Sala-I-Martin et al. (2004) and Sala-I-Martin (1997). See the references for more detailed descriptions and original sources of the variables.
WALS package for MATLAB (and Stata) provided on Jan Magnus' personal website. https://www.janmagnus.nl/items/WALS.pdf.
Magnus JR, Powell O, Prüfer P (2010).
“A comparison of two model averaging techniques with an application to growth empirics.”
Journal of Econometrics, 154(2), 139-153.
doi:10.1016/j.jeconom.2009.07.004.
Sala-I-Martin X (1997).
“I Just Ran Two Million Regressions.”
The American Economic Review, 87(2), 178–183.
Sala-I-Martin X, Doppelhofer G, Miller RI (2004).
“Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach.”
American Economic Review, 94(4), 813-835.
doi:10.1257/0002828042002570.
## Replicate Table 2 in Magnus et al. (2010)
# NOTE: prescale = FALSE, still used old version of WALS in Magnus et al. (2010).
# Not recommended anymore!
fitMPP <- wals(gdpgrowth ~ lgdp60 + equipinv + school60 + life60 + popgrowth |
law + tropics + avelf + confucian, data = GrowthMPP,
prior = laplace(), prescale = FALSE)
tableMPP <- cbind("coef" = coef(fitMPP), "se" = sqrt(diag(vcov(fitMPP))))
print(round(tableMPP, 4))