MaddisonData

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:

1. Find the countries with the highest gdppc for each year for which data are available using MaddisonLeaders(). Allow deleting companies with high gdppc based on something narrow like a commodity, e.g., oil. [DONE]

2. Support modeling a time series as c('level', 'growthRate').

2.1. Using the KFAS package with functions growthModel and growthUpdateFn plus plotting with ggplotPath and ggplotPath2 as documented in vignette KalmanSmoothing. [ALMOST DONE, but KFAS::fitSSM seems not to pass optional arguments to optim and strips dimnames from components of an SSModel. This means that with an irregular time series, e.g., data missing for some years, the gaps are ignored, and esimation assumes the data are contiguous. This impacts especially the state transition matrix T and transition variance Q. This should be a minor problem problem for series with only a few missing values and will be ignored until convenient to fix.]

2.2. Ask John Nash for his preferred substitute for optim. Also ask him about supporting an optional fixPar vector = NA for par to fix and the numeric values for others. This could support easy testing testing of submodels.

2.3. Ask Jouni Helske and the other KFAS and bssm contributors about supporting (a) dimnames and (b) an optional Time and dT component(s)) of an SSModel plus (c) a function asTime = Time component of a model else as.Posixct(names(y)) else as.Date(names(y)) else as.numeric(names(y)) else [ordered(names(y)) with dT =1:length(y)] withdT_ = diff(Time)anddT = c(dT[1], dT)and (d) forbssmassuming Student's t migrations ofgrowthRate`.

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

3. Plot the data available on gdppc and / 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:

4. Build a state space / Kalman models for gdppc and pop for each country in the Maddison project data.

5. Use Kalman smooth to interpolate and extrapolate (forward but not backwards) gdppc and pop for each country for all years that appear anywhere in the Maddison project data.

6. Identify the world leader in gdppc for each year, refining “1” using KFAS interpolation.

7. Identify the world technology leader for each year by evaluating the gdppc leader 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.

Installation

You can install the development version of MaddisonData from GitHub with:

# install.packages("pak")
pak::pak("sbgraves237/MaddisonData")

Example

[Coming soon.]

library(MaddisonData)
## basic example code