Long Run vs Short Run Decompositions in R

Load libraries

library(mFilter)
library(quantmod)

The Hodrick–Prescott Decomposition of Log of GDP per capita

For Japan

Import the data

getSymbols("RGDPCHJPA625NUPN",src="FRED")

[1] "RGDPCHJPA625NUPN"

Run the HP filter

hpf <- hpfilter(log(RGDPCHJPA625NUPN),freq = 6.25)

Plot the series

out <- xts(cbind(hpf$x, hpf$trend, hpf$cycle), index(RGDPCHJPA625NUPN))
colnames(out) <- c("x", "trend", "cycle")
par(mfrow = c(2, 1), mar = c(3, 2, 2, 1))
plot(out[,"x"], t= "n", main = paste(hpf$title, "of", hpf$xname))
lines(out[,"trend"], col = "red")

plot(out[,"cycle"], t = "n", main = "Cyclical component (deviations from trend)")

For Bolivia

getSymbols("RGDPCHBOA625NUPN",src="FRED")

[1] "RGDPCHBOA625NUPN"

hpf <- hpfilter(log(RGDPCHBOA625NUPN),freq = 6.25)
out <- xts(cbind(hpf$x, hpf$trend, hpf$cycle), index(RGDPCHBOA625NUPN))
colnames(out) <- c("x", "trend", "cycle")
par(mfrow = c(2, 1), mar = c(3, 2, 2, 1))
plot(out[,"x"], t= "n", main = paste(hpf$title, "of", hpf$xname))
lines(out[,"trend"], col = "red")

plot(out[,"cycle"], t = "n", main = "Cyclical component (deviations from trend)")