In pttry/ficomp: Short Run Price Competitiveness of Finland

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", 
  fig.height = 7,
  fig.width = 16,
  fig.path = "UCL_tot/", 
  echo = FALSE, message = FALSE, warning = FALSE
)

# install.packages("ameco")

devtools::load_all()
library(dplyr)
library(ameco)
library(tidyr)
library(ggplot2)
library(ggptt)

set_proj_theme(20)

# separate codes
ameco2 <- ameco %>% 
  tidyr::separate(code, into = c("country_code", NA, NA, NA, NA, "title_code"), sep = "\\.")

ameco2 %>% 
  distinct(title_code, title) %>% 
  # filter(grepl("djuste", title)) %>% 
  knitr::kable()

Terms of trade adjusted GDP

gdb_tot_series <- c("OVGDA", "OVGD", "UXGS", "PMGS", "OXGS")


fi_dat <- 
  ameco2 %>% 
  filter(country_code == "FIN",
         title_code %in% gdb_tot_series, 
         !grepl("ECU", unit))              # only EURO-national currency series

fi_dat %>% select(title_code, title, unit) %>% distinct() %>% knitr::kable()

fi_dat2 <- fi_dat %>% 
  select(title_code, year, value) %>% 
  spread(title_code, value) %>% 
  mutate(OVGDA2 = ((OVGD + UXGS/(PMGS/100) - OXGS) / OVGD[year == 2010]) * 100,
         OVGDA3 = (OVGD + UXGS/(PMGS/100) - OXGS),
         ovgd_10 = (OVGD / OVGD[year == 2010] * 100))

fi_dat2 %>% 
  gather(vars, value, - year) %>% 
  filter(vars %in% c("OVGDA2", "OVGDA", "ovgd_10")) %>% 
  ggplot(aes(year, value, colour = vars)) +
  geom_line()

fi_dat2 %>% 
  gather(vars, value, - year) %>% 
  filter(vars %in% c("OVGDA3", "OVGD")) %>% 
  ggplot(aes(year, value, colour = vars)) +
  geom_line()

Terms of trade adjusted NULC

ameco2 %>% 
  filter(title_code %in% c("UWCD", "OVGD", "OVGDA", "NWTD", "NETD"),
         country_code %in% c("FIN", "SWE", "DEU", "FRA"),
         !grepl("ECU", unit),
         !grepl("PPS", unit),
         year > 1989) %>%             # only EURO-national currency series
  select(country_code ,title_code, year, value) %>% 
  spread(title_code, value) %>% 
  group_by(country_code) %>% 
  mutate(nulc_per = rebase((UWCD / NWTD) / (OVGD / NETD), year, 2010),
         nulc_per_tota = rebase((UWCD / NWTD) / (OVGDA / NETD), year, 2010)) %>% 
  ungroup() %>% 
  select(country_code, year, nulc_per, nulc_per_tota) %>% 
  gather(vars, values, nulc_per, nulc_per_tota) %>% 
  ggplot(aes(year, values, colour = vars)) +
  facet_wrap(~ country_code) +
  geom_line() +
  the_title_blank()

data_ameco %>% 
  group_by(geo) %>% 
  mutate(B1GA2 = 100 * (B1GQ__CLV15_MNAC + 
                          (P6__CP_MNAC / (P7__CP_MNAC / P7__CLV15_MNAC)) - 
                           P6__CLV15_MNAC) /
                            B1GQ__CLV15_MNAC[time == 2015]) %>% 
  ungroup() %>% 
  filter(geo %in% c("FI", "US", "SE", "DE")) %>% 
  select(geo, time, B1GQA__CLV15_MNAC, B1GA2) %>% 
  gather(vars, values, -geo, -time) %>% 
  ggplot(aes(time, values, colour = vars)) +
  geom_line() +
  facet_wrap(~geo)