Make it easier for humans to access data from the Maddison Data Project
in R. Later releases may include vignettes, etc., documenting analyses
using the [KFAS] (Kalman filtering and smoothing, aka state space)
techniques with these data.
Objectives: Make it relatively easy in R to do the following:
-
Find the countries with the highest
gdppcfor each year for which data are available usingMaddisonLeaders(). -
Refine “1” by deleting companies with high
gdppcbased on something narrow like a commodity, e.g., oil, since 1600:
library(MaddisonData)
MadDat1600 <- subset(MaddisonData, year>1600)
Leaders1600 <- MaddisonLeaders(c('ARE', 'KWT', 'QAT'), data=MadDat1600)
summary(Leaders1600)
#> ISO yearBegin yearEnd n p
#> ARE ARE 1965 1984 5 0.2500000
#> AUS AUS 1853 1891 17 0.4358974
#> CHE CHE 1931 1934 4 1.0000000
#> GBR GBR 1808 1898 67 0.7362637
#> KWT KWT 1953 1957 5 1.0000000
#> LUX LUX 1991 1995 5 1.0000000
#> NLD NLD 1601 1807 207 1.0000000
#> NOR NOR 1996 2002 7 1.0000000
#> NZL NZL 1873 1874 2 1.0000000
#> QAT QAT 1950 2022 45 0.6164384
#> USA USA 1882 1990 58 0.5321101- Plot the data available on
gdppcand / or pop for a selection of countries, e.g., world leaders.
str(GBR_USA <- subset(MaddisonData::MaddisonData, ISO %in% c('GBR', 'USA')))
#> Classes 'tbl_df', 'tbl' and 'data.frame': 1004 obs. of 4 variables:
#> $ ISO : chr "GBR" "GBR" "GBR" "GBR" ...
#> $ year : num 1 1000 1252 1253 1254 ...
#> $ gdppc: num NA 1151 1320 1328 1317 ...
#> $ pop : num 800 2000 NA NA NA NA NA NA NA NA ...
GBR_USA1 <- MaddisonData::ggplotPath('year', 'gdppc', 'ISO', GBR_USA, 1000)
GBR_USA1+ggplot2::coord_cartesian(xlim=c(1500, 1850)) # for only 1500-1850 
GBR_USA1+ggplot2::coord_cartesian(xlim=c(1600, 1700), ylim=c(7, 17)) 
# label the lines
ISOll <- data.frame(x=c(1500, 1800), y=c(2.5, 1.7), label=c('GBR', 'USA'),
srt=c(0, 30), col=c('red', 'green'), size=c(2, 9))
GBR_USA2 <- ggplotPath('year', 'gdppc', 'ISO', GBR_USA, 1000,
labels=ISOll, fontsize = 20)
# h, vlines, manual legend only
Hlines <- c(1,3, 10, 30)
Vlines = c(1849, 1929, 1933, 1939, 1945)
(GBR_USA3 <- ggplotPath('year', 'gdppc', 'ISO', GBR_USA, 1000,
ylab='GDP per capita (2011 PPP K$)',
legend.position = NULL, hlines=Hlines, vlines=Vlines, labels=ISOll)) 
LATER:
-
Build a state space / Kalman models for
gdppcandpopfor each country in the Maddison project data. -
Use Kalman smooth to interpolate and extrapolate (forward but not backwards)
gdppcandpopfor each country for all years that appear anywhere in the Maddison project data. -
Identify the world leader in
gdppcfor each year, refining “1” usingKFASinterpolation. -
Identify the world technology leader for each year by evaluating the
gdppcleader for each year and replacing any whose leadership was narrow like members of OPEC with a country with a broad-based economy like the US.
You can install the development version of MaddisonData from GitHub with:
# install.packages("pak")
pak::pak("sbgraves237/MaddisonData")[Coming soon.]
library(MaddisonData)
## basic example code