In pttry/ficomp: Short Run Price Competitiveness of Finland
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.height = 6,
fig.width = 8,
fig.path = "Report/",
echo = FALSE, message = FALSE, warning = FALSE
)
# library(ficomp)
devtools::load_all()
library(dplyr)
library(forcats)
library(ggplot2)
library(tidyr)
library(ggptt)
set_proj_theme()
data(ulc_oecd_dat, q_dat, a_dat, naq_eurostat_dat, eo_q_dat)
base_year <- 2010
base_years <- 2000:2018
geos <- c(Finland = "FI", Sweden = "SE", Germany = "DE", France = "FR")
start_time <- "1996-01-01"
geo_trans <- geo_fi
h_geos <- c("FI", "SE", "DE", "US")
Neljännesvuositiedot
Neljännesvuositietoja on kolme erilaista tietokantaa:
- q_dat_oecd_ulc. OECD:n ULC-tietokantaan perustuva, joka sisältää laajimman maavalikoiman
vuodesta 1996 eteenpäin.
Alkuperäisissä tiedoissa on osalle maalle tietoja myös aiemmin.
- eo_q_dat. OECD:n Economic Outlook tietokantaan peruva, joka sisältää pisimmät
neljännesvuosittaiset sarjata. Saksan sarja alkaa 1991, joten tätä on käytetty aloitusvuonna.
Maavalikoima on suppeampi kuin ULC-tietokannan tiedoissa.
- q_dat. Eurostatin ja OECD:n kansantalouden tilinpidon tiedoista yhdistetty tietokanta.
Aloitusvuosi 1996, sisältää Eurostat maiden ulkopuolelta vain Yhdysvaltojen ja Japanin tiedot.
Tietokanta sisältää koko talouden lisäksi myös teollisuuden tiedot.
tibble(code = levels(naq_eurostat_dat$na_item), name = label_eurostat(levels(naq_eurostat_dat$na_item), "na_item"))
Nimellinen yksikkötyökustannus
OECD quarterly ULC data
OECD ULC tiedoissa olevat maat: r levels(ulc_oecd_dat$geo).
Maiden lukumäärä r length(levels(ulc_oecd_dat$geo)).
Muuttujat: r levels(ulc_oecd_dat$na_item)
ulc_oecd_dat %>%
filter(na_item == "NULC_APER") %>%
mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
h_geo = fct_relevel(h_geo, h_geos),
geo = fct_relevel(geo, rev(h_geos), after = Inf),
size = !(geo %in% h_geo)) %>%
translate(h_geo, geo_trans) %>%
ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) +
geom_line() +
scale_size_manual(values = c(2,1), guide = "none") +
scale_colour_manual(values = geo_col(length(h_geos) + 1)) +
the_title_blank()
# library(ggiraph)
#
# ggi <- last_plot() + geom_line_interactive(aes(tooltip = geo))
# girafe(print(ggi))
Tiedot alkavat pääosin vuodesta 1995. Aloittamalla vuodesta 1996 saadaan muutama muu maa mukaan.
Tietojen saatavuuden ja vertailtavuuden perusteella mukaan on otettu seuraavat maat.
ulc_oecd_dat %>%
filter(na_item == "NULC_APER") %>%
drop_na(values) %>%
group_by(geo, na_item) %>%
mutate(tmin = min(time),
tmax = max(time)) %>%
ungroup() %>%
mutate(selected = geo %in% c(eurostat_geos, oecd_geos_ulcq)) %>%
translate(geo, geo_trans) %>%
ggplot(aes(geo, ymin = tmin, ymax = tmax, colour = selected)) +
geom_linerange(size = 2) +
coord_flip() +
geom_hline(yintercept = as.numeric(as.Date("1996-01-01"))) +
scale_y_date(breaks = as.Date(c("1960-01-01", "1980-01-01",
"1996-01-01", "2000-01-01", "2020-01-01")),
date_labels = "%Y") +
scale_colour_manual(values = rev(geo_col(2)), guide = "none") +
the_title_blank("y")
save_fig("2_ulc_oecd_selected_geo", height = 5.8)
Valitut maat OECD quarterly
Indeksi ja suhteellinen indeksi IMF painoilla. y-akseli on katkaistu.
q_dat_oecd_ulc %>%
# filter(geo != "JP") %>%
select(geo, time, nulc_aper, nulc_aper_rel_imf) %>%
gather(vars, values, nulc_aper, nulc_aper_rel_imf) %>%
mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
h_geo = fct_relevel(h_geo, h_geos),
geo = fct_relevel(geo, rev(h_geos), after = Inf),
size = !(geo %in% h_geo)) %>%
translate(h_geo, geo_trans) %>%
translate(vars, c(nulc_aper = "Indeksi",
nulc_aper_rel_imf = "Suhteellinen indeksi")) %>%
ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) +
facet_wrap(~ vars) +
geom_line() +
scale_size_manual(values = c(2,1), guide = "none") +
scale_colour_manual(values = geo_col(length(h_geos) + 1)) +
the_title_blank(c("x", "l")) +
scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) +
labs(y = "indeksi, 2010 = 100")
save_fig("3_ulc_oecd")
OECD Economic Outlook
Alkaa 1991, mutta neljännesvuositieodoissa osasta maista ei tietoja. c("NO", "DK", "NZ", "ES")
eo_q_dat_large %>%
select(geo, time, values = nulc) %>%
group_by(geo) %>%
# coaleasce and na_if to set same min and ma for countries with all missing
mutate(tmin = coalesce(na_if(min(time[!is.na(values)]), Inf), as.Date("1996-01-01")),
tmax = coalesce(na_if(max(time[!is.na(values)]), -Inf), as.Date("1996-01-01"))) %>%
ungroup() %>%
mutate(selected = geo %in% c(eurostat_geos, oecd_geos_ulcq)) %>%
translate(geo, geo_trans) %>%
ggplot(aes(geo, ymin = tmin, ymax = tmax, colour = selected)) +
geom_linerange(size = 2, colour = geo_col(2)[1]) +
coord_flip() +
geom_hline(yintercept = as.numeric(as.Date("1991-01-01"))) +
scale_y_date(breaks = as.Date(c("1960-01-01", "1980-01-01",
"1991-01-01", "2000-01-01", "2020-01-01")),
date_labels = "%Y") +
scale_colour_manual(values = rev(geo_col(2)), guide = "none") +
the_title_blank("y")
save_fig("4_OECD_eo_geos")
Neljännesvuosidata
Yhdistelmä Eurostatin ja OECD:n Quarterly National Accounts datasta
q_dat %>%
select(geo, time, values = nulc_aper) %>%
group_by(geo) %>%
mutate(tmin = min(time[!is.na(values)]),
tmax = max(time[!is.na(values)])) %>%
ungroup() %>%
mutate(selected = geo %in% c(eurostat_geos, oecd_geos_ulcq)) %>%
translate(geo, geo_trans) %>%
ggplot(aes(geo, ymin = tmin, ymax = tmax, colour = selected)) +
geom_linerange(size = 2, colour = geo_col(2)[1]) +
coord_flip() +
geom_hline(yintercept = as.numeric(as.Date("1996-01-01"))) +
scale_y_date(breaks = as.Date(c("1996-01-01", "2000-01-01",
"2020-01-01")),
date_labels = "%Y") +
scale_colour_manual(values = rev(geo_col(2)), guide = "none") +
the_title_blank("y")
save_fig("5_Eurostat_OECD_geos")
data_quartely_est %>%
select(geo, time, values = nulc_aper) %>%
group_by(geo) %>%
mutate(tmin = min(time[!is.na(values)]),
tmax = max(time[!is.na(values)])) %>%
ungroup() %>%
mutate(selected = geo %in% c(eurostat_geos, oecd_geos_ulcq)) %>%
translate(geo, geo_trans) %>%
ggplot(aes(geo, ymin = tmin, ymax = tmax, colour = selected)) +
geom_linerange(size = 2, colour = geo_col(2)[1]) +
coord_flip() +
geom_hline(yintercept = as.numeric(as.Date("1996-01-01"))) +
scale_y_date(breaks = as.Date(c("1996-01-01", "2000-01-01",
"2020-01-01")),
date_labels = "%Y") +
scale_colour_manual(values = rev(geo_col(2)), guide = "none") +
the_title_blank("y")
q_dat %>%
# filter(geo != "JP") %>%
select(geo, time, nulc_aper, nulc_aper_rel_imf) %>%
gather(vars, values, nulc_aper, nulc_aper_rel_imf) %>%
mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
h_geo = fct_relevel(h_geo, h_geos),
geo = fct_relevel(geo, rev(h_geos), after = Inf),
size = !(geo %in% h_geo)) %>%
translate(h_geo, geo_trans) %>%
translate(vars, c(nulc_aper = "Indeksi",
nulc_aper_rel_imf = "Suhteellinen indeksi")) %>%
ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) +
facet_wrap(~ vars) +
geom_line() +
scale_size_manual(values = c(2,1), guide = "none") +
scale_colour_manual(values = geo_col(length(h_geos) + 1)) +
the_title_blank(c("x", "l")) +
scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) +
labs(y = "indeksi, 2010 = 100")
save_fig("6_nulc_q_dat")
Vertailu OECD ULC ja yhdistelmä
Economic outlook antaa alkupäässä ja lopussa hieman erilaisia arvoja.
max_time_q <- max(q_dat$time)
bind_rows("Eurostat ja OECD QNA" = q_dat,
"OECD ULC" = q_dat_oecd_ulc,
"OECD EO" = rename(eo_q_dat, nulc_aper = nulc, nulc_aper_rel_imf = nulc_rel_imf),
.id = "source") %>%
filter(geo == "FI",
# time >= start_time,
time <= max_time_q) %>%
select(source, time, nulc_aper, nulc_aper_rel_imf) %>%
gather(vars, values, nulc_aper, nulc_aper_rel_imf) %>%
# mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
# h_geo = fct_relevel(h_geo, h_geos),
# geo = fct_relevel(geo, rev(h_geos), after = Inf),
# size = !(geo %in% h_geo)) %>%
# translate(h_geo, geo_trans) %>%
translate(vars, c(nulc_aper = "Indeksi",
nulc_aper_rel_imf = "Suhteellinen indeksi")) %>%
ggplot(aes(time, values, colour = source)) +
facet_wrap(~ vars) +
geom_line() +
# scale_size_manual(values = c(2,1), guide = "none") +
# scale_colour_manual(values = geo_col(length(h_geos) + 1)) +
the_title_blank(c("x", "l")) +
# scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) +
labs(y = "indeksi, 2010 = 100") +
the_legend_bot()
save_fig("7_nulc_comparison")
OECD EO:n ero tulee siitä, että siinä ei ole laskettu yrittäjäkorjausta.
bind_rows("Eurostat ja OECD QNA" = q_dat,
"OECD EO" = eo_q_dat,
.id = "source") %>%
filter(geo == "FI",
time <= max_time_q) %>%
select(source, time, nulc_aper, nulc) %>%
gather(vars, values, -source, - time) %>%
filter(!is.na(values)) %>%
unite(vars, vars, source, sep = ", ") %>%
ggplot(aes(time, values, colour = vars)) +
geom_line() +
the_title_blank(c("x", "l")) +
labs(y = "indeksi, 2010 = 100") +
guides(color = guide_legend(nrow = 2)) +
the_legend_bot()
# To check
bind_rows("Eurostat ja EO" = q_dat,
"OECD ULC" = q_dat_oecd_ulc, .id = "source") %>%
select(geo, source, time, nulc_aper, nulc_aper_rel_imf) %>%
gather(vars, values, nulc_aper, nulc_aper_rel_imf) %>%
# mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
# h_geo = fct_relevel(h_geo, h_geos),
# geo = fct_relevel(geo, rev(h_geos), after = Inf),
# size = !(geo %in% h_geo)) %>%
# translate(h_geo, geo_trans) %>%
mutate(source = fct_rev(as_factor(source))) %>%
translate(vars, c(nulc_aper = "Indeksi",
nulc_aper_rel_imf = "Suhteellinen indeksi")) %>%
ggplot(aes(time, values, colour = source)) +
facet_grid(geo ~ vars) +
geom_line() +
# scale_size_manual(values = c(2,1), guide = "none") +
# scale_colour_manual(values = geo_col(length(h_geos) + 1)) +
the_title_blank(c("x", "l")) +
# scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) +
labs(y = "indeksi, 2010 = 100")
Koko talous ja teollisuus neljännesvuositiedoissa
Tässä vain eurostat maat
data_main_groups_q %>%
select(geo, nace0, time, nulc_aper = nulc_aper_va, nulc_aper_rel_imf = nulc_aper_va_rel_imf) %>%
gather(vars, values, nulc_aper, nulc_aper_rel_imf) %>%
mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
h_geo = fct_relevel(h_geo, h_geos),
geo = fct_relevel(geo, rev(h_geos), after = Inf),
size = !(geo %in% h_geo)) %>%
translate(h_geo, geo_trans) %>%
translate(vars, c(nulc_aper = "Indeksi",
nulc_aper_rel_imf = "Suhteellinen indeksi")) %>%
translate(nace0, c(total = "Koko talous", manu = "Teollisuus")) %>%
ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) +
facet_grid(nace0 ~ vars) +
geom_line() +
scale_size_manual(values = c(2,1), guide = "none") +
scale_colour_manual(values = geo_col(length(h_geos))) +
the_title_blank(c("x", "l")) +
scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) +
labs(y = "indeksi, 2010 = 100")
save_fig("8_nulc_q_total_manu")
bind_rows(annual = mutate(data_main_groups_a, time = as.Date(paste0(time, "-07-01"))),
quarterly = data_main_groups_q,
ilc = mutate(data_ilc, time = as.Date(paste0(time, "-07-01"))),
.id = "source") %>%
select(geo, time, nace0, source, nulc_va_eur) %>%
mutate(nace0 = recode(nace0, manu_ex26 = "manu_ex26_27")) %>%
filter(nace0 == "manu") %>%
group_by(source) %>%
weight_at(geo, time, at = c("nulc_va_eur"), weight_df = weights_ecfin42) %>%
ungroup() %>%
gather(vars, values, -geo, -time, -source,-nace0) %>%
filter(geo == "FI") %>%
ggplot(aes(time, values, color = source)) +
geom_line() +
facet_wrap(~ vars)
bind_rows(annual = mutate(data_main_groups_a, time = as.Date(paste0(time, "-07-01"))),
quarterly = data_main_groups_q,
ilc = mutate(data_ilc, time = as.Date(paste0(time, "-07-01"))),
.id = "source") %>%
select(geo, time, nace0, source, nulc_va, lp_ind, d1_per_ind) %>%
filter(nace0 == "manu") %>%
gather(vars, values, -geo, -time, -source,-nace0) %>%
filter(geo == "FI") %>%
ggplot(aes(time, values, color = source)) +
geom_line() +
facet_wrap(~ vars)
data_quartely_manu_est %>%
select(geo, time, nace0, nulc_va_eur, nulc_va_eur_long, nulc_va_eur_rel_ecfin21, nulc_va_eur_long_rel_ecfin21) %>%
filter(nace0 == "manu") %>%
gather(vars, values, -geo, -time, -nace0) %>%
filter(geo == "FI") %>%
ggplot(aes(time, values, color = vars)) +
geom_line()
Different measures quarterly
Different definations for unit labour cost:
Output: GDP or value added
Relation to hours worked or persons (relevant if adjusted for self-employed or for decomposition)
# lc_dat_nace <-
# naq_eurostat_dat %>%
# unite(vars, na_item, unit) %>%
# # filter(vars %in% c("D1_CP_MNAC", "B1G_CLV10_MNAC")) %>%
# spread(vars, values) %>%
# group_by(geo, nace_r2) %>%
# mutate(
# # nominal unit labour cost
# nulc_va = ind_ulc(D1_CP_MNAC, B1G_CLV10_MNAC, time = time, baseyear = base_year),
# nulc = ind_ulc(D1_CP_MNAC, B1GQ_CLV10_MNAC, time = time, baseyear = base_year),
# # nominal unit labour cost, adjusted to take account emploees/emplyed share
# nulc_hw_va = ind_ulc(D1_CP_MNAC / SAL_DC_THS_HW, B1G_CLV10_MNAC / EMP_DC_THS_HW, time = time, baseyear = base_year),
# nulc_hw = ind_ulc(D1_CP_MNAC / SAL_DC_THS_HW, B1GQ_CLV10_MNAC / EMP_DC_THS_HW, time = time, baseyear = base_year),
# nulc_hw_eur = ind_ulc(D1_CP_MEUR / SAL_DC_THS_HW, B1GQ_CLV10_MNAC / EMP_DC_THS_HW, time = time, baseyear = base_year),
# nulc_per_va = ind_ulc(D1_CP_MNAC / SAL_DC_THS_PER, B1G_CLV10_MNAC / EMP_DC_THS_PER, time = time, baseyear = base_year),
# nulc_per = ind_ulc(D1_CP_MNAC / SAL_DC_THS_PER, B1GQ_CLV10_MNAC / EMP_DC_THS_PER, time = time, baseyear = base_year)) %>%
# ungroup()
#
# lc_dat <- lc_dat_nace %>%
# filter(nace_r2 == "TOTAL")
q_dat %>%
filter(geo %in% h_geos,
time >= start_time) %>%
select(geo, time, nulc, nulc_va, nulc_hw, nulc_hw_va, nulc_aper, nulc_aper_va) %>%
gather(ind, values, -geo, -time) %>%
ggplot(aes(time, values, colour = geo)) +
geom_line() +
facet_wrap(~ ind) +
the_title_blank()
On short term a NULC indicator selection does not matter. On longer term
differences acculumate.
q_dat %>%
filter(geo == "FI",
time >= start_time) %>%
select(geo, time, nulc, nulc_va, nulc_hw, nulc_hw_va, nulc_aper, nulc_aper_va) %>%
gather(ind, values, -geo, -time) %>%
group_by(geo, ind) %>%
mutate(change = 100 * (values / lag(values, 4, order_by = time) - 1)) %>%
ungroup() %>%
gather(vars, values, values, change) %>%
translate(vars, c(change = "Muutos, %", values = "Taso, indeksi 2010 = 100")) %>%
translate(ind, var_labels_fi, simple = TRUE, parts = c(4,5)) %>%
ggplot(aes(time, values, colour = ind)) +
facet_wrap(~ vars, scales = "free_y") +
geom_line() +
geom_hline(aes(yintercept = yint),
data = data.frame(yint = 0, vars = "Muutos, %")) +
the_title_blank() +
the_legend_bot() +
guides(colour = guide_legend(nrow = 3))
save_fig("9_nulc_ind_comparison")
fast_geos <-
data_quartely_est %>%
filter(time == max(time) & !is.na(nulc_aper_eur_long)) %>%
pull(geo)
dat_eff <-
ert_eff_ic_q %>%
select(geo, time, exch_rt, values) %>%
spread(exch_rt, values)
data_quartely_est_extra <-
data_quartely_est %>%
left_join(dat_eff, by = c("geo", "time")) %>%
# left_join(data_quartely_manu_est, by = c("geo", "time"), suffix = c("", "_manu")) %>%
group_by(time) %>%
mutate(nulc_aper_eur_long_rel_ecfin14_extra =
weight_index2(nulc_aper_eur_long, geo, time,
geos = setdiff(eurostat_geos, c("NL", "EL", "IE")), weight_df = weights_ecfin37)) %>%
mutate(nulc_aper_eur_long_rel_fast =
weight_index2(nulc_aper_eur_long, geo, time,
geos = fast_geos, weight_df = weights_ecfin37)) %>%
mutate(rulc_aper_long_rel_fast =
weight_index2(rulc_aper_long, geo, time,
geos = fast_geos, weight_df = weights_ecfin37)) %>%
ungroup()
data_quartely_est_extra %>%
select(geo, time,
# "14 maata" = nulc_aper_eur_long_rel_ecfin14_extra,
"20 maata" = nulc_aper_eur_long_rel_ecfin20,
"10 maata" = nulc_aper_eur_long_rel_fast,
# "EU komission" = REER_IC37_ULCT,
"reaalinen, 10 maata" = rulc_aper_long_rel_fast,
"reaalinen, 20 maata" = rulc_aper_long_rel_ecfin20,) %>%
filter(geo == "FI",
time >= start_time) %>%
gather(ind, values, -geo, -time) %>%
mutate(ind = fct_rev(ind)) %>%
mutate(time = halfq_shift(time)) %>%
ggplot(aes(time, values, colour = ind)) +
geom_line() +
geom_hline(yintercept = 100) +
the_title_blank(c("x", "l")) +
the_legend_bot() +
guides(colour = guide_legend()) +
labs(title = "Suomen suhteelliset yksikkötyökustannukset samassa valuutassa",
subtitle = "eli efektiivinen valuuttakurssi yksikkötyökustannuksilla.
Vertailussa komission indikaattori ja aidosta neljännesvuositiededosta
lasketut 20 ja 10 maan tietoihin perustuvat indikaattorit",
y = "indeksi, 2010 = 100",
caption = "Lähde: ECFIN, AMECO, Eurostat, OECD, PTT")
# data_quartely_est %>%
# select(geo, time, nulc_aper_eur_long) %>%
# spread(geo, nulc_aper_eur_long) %>%
# View()
ggsave_twitter("nulc.png")
data_quartely_est_fi <-
data_quartely_est %>%
filter(geo == "FI")
data_quartely_est_extra_fi <-
data_quartely_est_extra %>%
filter(geo == "FI")
library(MacrobondAPI)
mb_nulc_aper_eur_long_rel_fast <- MacrobondAPI::CreateTimeSeriesObject(name = "ih:mb:com:nulcefast",
description = "Suomen nimelliset suhteelliset yksikkötyökustannukset samassa valuutassa, 10 maata",
region = "fi",
category = "National accounts",
frequency = "Quarterly",
startDateOrDates = as.Date(data_quartely_est_extra_fi$time),
values = data_quartely_est_extra_fi$nulc_aper_eur_long_rel_fast
)
mb_nulc_aper_eur_long_rel_ecfin14 <- MacrobondAPI::CreateTimeSeriesObject(name = "ih:mb:com:nulcecfin14",
description = "Suomen nimelliset suhteelliset yksikkötyökustannukset samassa valuutassa, 14 maata",
region = "fi",
category = "National accounts",
frequency = "Quarterly",
startDateOrDates = as.Date(data_quartely_est_fi$time),
values = data_quartely_est_fi$nulc_aper_eur_long_rel_ecfin14
)
mb_nulc_aper_eur_long_rel_ecfin20 <- MacrobondAPI::CreateTimeSeriesObject(name = "ih:mb:com:nulcecfin20",
description = "Suomen nimelliset suhteelliset yksikkötyökustannukset samassa valuutassa, 20 maata",
region = "fi",
category = "National accounts",
frequency = "Quarterly",
startDateOrDates = data_quartely_est_fi$time[!is.na(data_quartely_est_fi$nulc_aper_eur_long_rel_ecfin20)],
values = data_quartely_est_fi$nulc_aper_eur_long_rel_ecfin20[!is.na(data_quartely_est_fi$nulc_aper_eur_long_rel_ecfin20)]
)
mb_rulc_aper_long_rel_fast <- MacrobondAPI::CreateTimeSeriesObject(name = "ih:mb:com:rulcfast",
description = "Suomen reaaliset suhteelliset yksikkötyökustannukset, 10 maata",
region = "fi",
category = "National accounts",
frequency = "Quarterly",
startDateOrDates = data_quartely_est_extra_fi$time[!is.na(data_quartely_est_extra_fi$rulc_aper_long_rel_fast)],
values = data_quartely_est_extra_fi$rulc_aper_long_rel_fast[!is.na(data_quartely_est_extra_fi$rulc_aper_long_rel_fast)]
)
UploadOneOrMoreTimeSeries(list(mb_nulc_aper_eur_long_rel_fast, mb_nulc_aper_eur_long_rel_ecfin20, mb_rulc_aper_long_rel_fast))
q_dat %>%
select(time, geo, "Palkasaajakorvaukset" = d1_hw_ind, "Yksikkötyökustannukset" = nulc_hw_va_eur, "Työn tuottavuus" = lp_hw_ind) %>%
gather(vars, values, -geo, -time) %>%
group_by(geo, vars) %>%
mutate(values = rebase(values, time, 2015)) %>%
ungroup() %>%
filter(geo %in% c(geos, "NO", "DK", "NL", "IT", "ES"), time >= "2015-01-01") %>%
ggplot(aes(time, values, color = geo)) +
facet_wrap(~ vars) +
geom_line() +
labs(y = "indeksi, 2015, Q1 = 100")
q_dat %>%
select(time, geo, d1_hw_ind) %>%
gather(vars, values, -geo, -time) %>%
group_by(geo, vars) %>%
mutate(values = rebase(values, time, 2015)) %>%
ungroup() %>%
filter(geo %in% c(geos, "NO", "DK", "NL", "IT", "ES"), time >= "2015-01-01") %>%
ggplot(aes(time, values, color = geo)) +
facet_wrap(~ vars) +
geom_line()
Omassa ja samassa valuutassa
Samassa valuutassa volatiliteetti paljon suurempi.
q_dat %>%
filter(geo %in% h_geos,
time >= start_time) %>%
select(geo, time, nulc, nulc_eur) %>%
gather(ind, values, -geo, -time, factor_key = TRUE) %>%
ggplot(aes(time, values, colour = geo)) +
geom_line() +
facet_wrap(~ ind) +
the_title_blank()
Sama suhteellisina.
q_dat %>%
filter(geo %in% h_geos,
time >= start_time) %>%
select(geo, time, nulc_rel_imf, nulc_eur_rel_imf) %>%
gather(ind, values, -geo, -time, factor_key = TRUE) %>%
ggplot(aes(time, values, colour = geo)) +
geom_line() +
facet_wrap(~ ind) +
the_title_blank()
# Nominal unit labour cost comparison from different sources
# NULC_APER_OECD = NULC_PER_ECB = nulc_per
# = (D1_CP_MNAC / SAL_DC_THS_PER) / (B1GQ_CLV10_MNAC / EMP_DC_THS_PER)
#
# NULC_HW_ECB = nulc_hw
# = (D1_CP_MNAC / SAL_DC_THS_HW) / (B1GQ_CLV10_MNAC / EMP_DC_THS_HW)
ulc_oecd_with <- ulc_oecd_dat %>%
select(- unit) %>%
filter(na_item == "NULC_APER") %>%
mutate(na_item = paste0(na_item, "_OECD")) %>%
group_by(geo, na_item) %>%
mutate(values = rebase(values, time, base_year)) %>%
ungroup() %>%
spread(na_item, values)
ulc_eurostat_with <- ulc_eurostat_dat %>%
filter(na_item %in% c("NULC_HW", "NULC_PER")) %>%
select(time, geo, na_item, values) %>%
mutate(na_item = paste0(na_item, "_eurostat")) %>%
group_by(geo, na_item) %>%
mutate(values = rebase(values, time, base_year)) %>%
ungroup() %>%
spread(na_item, values)
ulc_ecb_with <- ulc_ecb_dat %>%
filter(na_item %in% c("NULC_HW", "NULC_PER")) %>%
select(time, geo, na_item, values) %>%
mutate(na_item = paste0(na_item, "_ECB")) %>%
group_by(geo, na_item) %>%
mutate(values = rebase(values, time, base_year)) %>%
ungroup() %>%
spread(na_item, values)
lc_with <- lc_dat %>%
select(time, geo, nulc, nulc_va, nulc_hw, nulc_hw_va, nulc_per, nulc_per_va) %>%
left_join(ulc_oecd_with, by = c("time", "geo")) %>%
# left_join(ulc_eurostat_with, by = c("time", "geo")) %>%
left_join(ulc_ecb_with, by = c("time", "geo")) # %>% filter(geo == "FI") %>% View()
lc_with %>%
gather(vars, values, - time, - geo) %>%
mutate(vars = fct_relevel(vars, "NULC_APER", after = Inf)) %>%
filter(geo == "FI") %>%
ggplot(aes(time, values, colour = vars)) +
geom_line()
Painotus
Suomelle painoilla ei juuri väliä.
q_dat %>%
filter(geo %in% h_geos) %>%
select(geo, time, nulc_aper_rel15, nulc_aper_rel_bis, nulc_aper_rel_imf) %>%
gather(vars, values, -geo, -time) %>%
# mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
# geo = fct_relevel(geo, h_geos, after = Inf)) %>%
translate(geo, geo_fi) %>%
translate(vars, c(nulc_aper_rel15 = "BIS, kiinteä 2015",
nulc_aper_rel_bis = "BIS",
nulc_aper_rel_imf = "IMF")) %>%
ggplot(aes(time, values, colour = vars)) +
geom_line() +
facet_wrap(~ geo, scales = "free") +
scale_size_manual(values = c(2,1)) +
the_title_blank()
save_fig("Q_weights")
Vertailu muuttujiin
q_dat %>%
filter(geo %in% h_geos) %>%
select(geo, time, nulc_aper_rel_imf, gdp_ind_rel_imf, exp_ind_rel_imf, XPERF) %>%
mutate(nulc_aper_rel_imf = rebase(1/nulc_aper_rel_imf, time, base_year),
XPERF = 100* XPERF) %>%
gather(vars, values, -geo, -time) %>%
# mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
# geo = fct_relevel(geo, h_geos, after = Inf)) %>%
translate(geo, geo_fi) %>%
translate(vars, c(nulc_aper_rel_imf = "ULCy, käänteinen", gdp_ind_rel_imf = "BKT",
exp_ind_rel_imf = "Vienti", XPERF = "Vientimenestys")) %>%
ggplot(aes(time, values, colour = vars)) +
geom_line() +
facet_wrap(~ geo, scales = "free") +
scale_size_manual(values = c(2,1)) +
the_title_blank()
save_fig("Q_ulc_gdp_comp")
data_quartely_est %>%
filter(geo == "FI", time >= "1995-01-01") %>%
select(geo, time, nulc_var = nulc_aper_long_rel_ecfin20, gdp_ind_long_rel_ecfin20, exp_ind_long_rel_ecfin20, XPERF) %>%
mutate(nulc_var = rebase(1/(nulc_var), time, base_year),
XPERF = rebase(XPERF, time, base_year)) %>%
gather(vars, values, -geo, -time) %>%
# mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
# geo = fct_relevel(geo, h_geos, after = Inf)) %>%
translate(geo, geo_fi) %>%
# translate(vars, c(nulc_aper_rel_imf = "ULCy, käänteinen", gdp_ind_rel_imf = "BKT",
# exp_ind_rel_imf = "Vienti", XPERF = "Vientimenestys")) %>%
ggplot(aes(time, values, colour = vars)) +
geom_line() +
# facet_wrap(~ geo, scales = "free") +
scale_size_manual(values = c(2,1)) +
scale_y_continuous(sec.axis = sec_axis(~ . * -1)) +
the_title_blank() +
the_legend_bot() +
guides(colour = guide_legend(nrow = 2))
save_board_figs("rel")
data_ameco_long %>%
filter(geo == "FI") %>%
filter(time >= "1995-01-01") %>%
select(geo, time, nulc_var = nulc_aper_rel_ameco15,
# rulc_var = rulc_aper_rel_ameco15,
out_var = gdp_ind_rel_ameco15) %>%
mutate(nulc_var = rebase(1/nulc_var, time, base_year)) %>%
# mutate(rulc_var = rebase(1/rulc_var, time, base_year)) %>%
# mutate(out_var = 100* (out_var / lag(out_var) -1) + 100) %>%
gather(vars, values, -geo, -time) %>%
# mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"),
# geo = fct_relevel(geo, h_geos, after = Inf)) %>%
translate(geo, geo_fi) %>%
# translate(vars, c(nulc_aper_rel_imf = "ULCy, käänteinen", gdp_ind_rel_imf = "BKT",
# exp_ind_rel_imf = "Vienti", XPERF = "Vientimenestys")) %>%
ggplot(aes(time, values, colour = vars)) +
geom_line() +
facet_wrap(~ geo, scales = "free") +
scale_size_manual(values = c(2,1)) +
the_title_blank()
pttry/ficomp documentation built on June 11, 2024, 9:53 p.m.
R Package Documentation
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.height = 6, fig.width = 8, fig.path = "Report/", echo = FALSE, message = FALSE, warning = FALSE ) # library(ficomp) devtools::load_all() library(dplyr) library(forcats) library(ggplot2) library(tidyr) library(ggptt) set_proj_theme() data(ulc_oecd_dat, q_dat, a_dat, naq_eurostat_dat, eo_q_dat) base_year <- 2010 base_years <- 2000:2018 geos <- c(Finland = "FI", Sweden = "SE", Germany = "DE", France = "FR") start_time <- "1996-01-01" geo_trans <- geo_fi h_geos <- c("FI", "SE", "DE", "US")
Neljännesvuositiedot
Neljännesvuositietoja on kolme erilaista tietokantaa:
- q_dat_oecd_ulc. OECD:n ULC-tietokantaan perustuva, joka sisältää laajimman maavalikoiman vuodesta 1996 eteenpäin. Alkuperäisissä tiedoissa on osalle maalle tietoja myös aiemmin.
- eo_q_dat. OECD:n Economic Outlook tietokantaan peruva, joka sisältää pisimmät neljännesvuosittaiset sarjata. Saksan sarja alkaa 1991, joten tätä on käytetty aloitusvuonna. Maavalikoima on suppeampi kuin ULC-tietokannan tiedoissa.
- q_dat. Eurostatin ja OECD:n kansantalouden tilinpidon tiedoista yhdistetty tietokanta. Aloitusvuosi 1996, sisältää Eurostat maiden ulkopuolelta vain Yhdysvaltojen ja Japanin tiedot. Tietokanta sisältää koko talouden lisäksi myös teollisuuden tiedot.
tibble(code = levels(naq_eurostat_dat$na_item), name = label_eurostat(levels(naq_eurostat_dat$na_item), "na_item"))
Nimellinen yksikkötyökustannus
OECD quarterly ULC data
OECD ULC tiedoissa olevat maat: r levels(ulc_oecd_dat$geo).
Maiden lukumäärä r length(levels(ulc_oecd_dat$geo)).
Muuttujat: r levels(ulc_oecd_dat$na_item)
ulc_oecd_dat %>% filter(na_item == "NULC_APER") %>% mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), h_geo = fct_relevel(h_geo, h_geos), geo = fct_relevel(geo, rev(h_geos), after = Inf), size = !(geo %in% h_geo)) %>% translate(h_geo, geo_trans) %>% ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) + geom_line() + scale_size_manual(values = c(2,1), guide = "none") + scale_colour_manual(values = geo_col(length(h_geos) + 1)) + the_title_blank() # library(ggiraph) # # ggi <- last_plot() + geom_line_interactive(aes(tooltip = geo)) # girafe(print(ggi))
Tiedot alkavat pääosin vuodesta 1995. Aloittamalla vuodesta 1996 saadaan muutama muu maa mukaan. Tietojen saatavuuden ja vertailtavuuden perusteella mukaan on otettu seuraavat maat.
ulc_oecd_dat %>% filter(na_item == "NULC_APER") %>% drop_na(values) %>% group_by(geo, na_item) %>% mutate(tmin = min(time), tmax = max(time)) %>% ungroup() %>% mutate(selected = geo %in% c(eurostat_geos, oecd_geos_ulcq)) %>% translate(geo, geo_trans) %>% ggplot(aes(geo, ymin = tmin, ymax = tmax, colour = selected)) + geom_linerange(size = 2) + coord_flip() + geom_hline(yintercept = as.numeric(as.Date("1996-01-01"))) + scale_y_date(breaks = as.Date(c("1960-01-01", "1980-01-01", "1996-01-01", "2000-01-01", "2020-01-01")), date_labels = "%Y") + scale_colour_manual(values = rev(geo_col(2)), guide = "none") + the_title_blank("y") save_fig("2_ulc_oecd_selected_geo", height = 5.8)
Valitut maat OECD quarterly
Indeksi ja suhteellinen indeksi IMF painoilla. y-akseli on katkaistu.
q_dat_oecd_ulc %>% # filter(geo != "JP") %>% select(geo, time, nulc_aper, nulc_aper_rel_imf) %>% gather(vars, values, nulc_aper, nulc_aper_rel_imf) %>% mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), h_geo = fct_relevel(h_geo, h_geos), geo = fct_relevel(geo, rev(h_geos), after = Inf), size = !(geo %in% h_geo)) %>% translate(h_geo, geo_trans) %>% translate(vars, c(nulc_aper = "Indeksi", nulc_aper_rel_imf = "Suhteellinen indeksi")) %>% ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) + facet_wrap(~ vars) + geom_line() + scale_size_manual(values = c(2,1), guide = "none") + scale_colour_manual(values = geo_col(length(h_geos) + 1)) + the_title_blank(c("x", "l")) + scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) + labs(y = "indeksi, 2010 = 100") save_fig("3_ulc_oecd")
OECD Economic Outlook
Alkaa 1991, mutta neljännesvuositieodoissa osasta maista ei tietoja. c("NO", "DK", "NZ", "ES")
eo_q_dat_large %>% select(geo, time, values = nulc) %>% group_by(geo) %>% # coaleasce and na_if to set same min and ma for countries with all missing mutate(tmin = coalesce(na_if(min(time[!is.na(values)]), Inf), as.Date("1996-01-01")), tmax = coalesce(na_if(max(time[!is.na(values)]), -Inf), as.Date("1996-01-01"))) %>% ungroup() %>% mutate(selected = geo %in% c(eurostat_geos, oecd_geos_ulcq)) %>% translate(geo, geo_trans) %>% ggplot(aes(geo, ymin = tmin, ymax = tmax, colour = selected)) + geom_linerange(size = 2, colour = geo_col(2)[1]) + coord_flip() + geom_hline(yintercept = as.numeric(as.Date("1991-01-01"))) + scale_y_date(breaks = as.Date(c("1960-01-01", "1980-01-01", "1991-01-01", "2000-01-01", "2020-01-01")), date_labels = "%Y") + scale_colour_manual(values = rev(geo_col(2)), guide = "none") + the_title_blank("y") save_fig("4_OECD_eo_geos")
Neljännesvuosidata
Yhdistelmä Eurostatin ja OECD:n Quarterly National Accounts datasta
q_dat %>% select(geo, time, values = nulc_aper) %>% group_by(geo) %>% mutate(tmin = min(time[!is.na(values)]), tmax = max(time[!is.na(values)])) %>% ungroup() %>% mutate(selected = geo %in% c(eurostat_geos, oecd_geos_ulcq)) %>% translate(geo, geo_trans) %>% ggplot(aes(geo, ymin = tmin, ymax = tmax, colour = selected)) + geom_linerange(size = 2, colour = geo_col(2)[1]) + coord_flip() + geom_hline(yintercept = as.numeric(as.Date("1996-01-01"))) + scale_y_date(breaks = as.Date(c("1996-01-01", "2000-01-01", "2020-01-01")), date_labels = "%Y") + scale_colour_manual(values = rev(geo_col(2)), guide = "none") + the_title_blank("y") save_fig("5_Eurostat_OECD_geos")
data_quartely_est %>% select(geo, time, values = nulc_aper) %>% group_by(geo) %>% mutate(tmin = min(time[!is.na(values)]), tmax = max(time[!is.na(values)])) %>% ungroup() %>% mutate(selected = geo %in% c(eurostat_geos, oecd_geos_ulcq)) %>% translate(geo, geo_trans) %>% ggplot(aes(geo, ymin = tmin, ymax = tmax, colour = selected)) + geom_linerange(size = 2, colour = geo_col(2)[1]) + coord_flip() + geom_hline(yintercept = as.numeric(as.Date("1996-01-01"))) + scale_y_date(breaks = as.Date(c("1996-01-01", "2000-01-01", "2020-01-01")), date_labels = "%Y") + scale_colour_manual(values = rev(geo_col(2)), guide = "none") + the_title_blank("y")
q_dat %>% # filter(geo != "JP") %>% select(geo, time, nulc_aper, nulc_aper_rel_imf) %>% gather(vars, values, nulc_aper, nulc_aper_rel_imf) %>% mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), h_geo = fct_relevel(h_geo, h_geos), geo = fct_relevel(geo, rev(h_geos), after = Inf), size = !(geo %in% h_geo)) %>% translate(h_geo, geo_trans) %>% translate(vars, c(nulc_aper = "Indeksi", nulc_aper_rel_imf = "Suhteellinen indeksi")) %>% ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) + facet_wrap(~ vars) + geom_line() + scale_size_manual(values = c(2,1), guide = "none") + scale_colour_manual(values = geo_col(length(h_geos) + 1)) + the_title_blank(c("x", "l")) + scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) + labs(y = "indeksi, 2010 = 100") save_fig("6_nulc_q_dat")
Vertailu OECD ULC ja yhdistelmä
Economic outlook antaa alkupäässä ja lopussa hieman erilaisia arvoja.
max_time_q <- max(q_dat$time) bind_rows("Eurostat ja OECD QNA" = q_dat, "OECD ULC" = q_dat_oecd_ulc, "OECD EO" = rename(eo_q_dat, nulc_aper = nulc, nulc_aper_rel_imf = nulc_rel_imf), .id = "source") %>% filter(geo == "FI", # time >= start_time, time <= max_time_q) %>% select(source, time, nulc_aper, nulc_aper_rel_imf) %>% gather(vars, values, nulc_aper, nulc_aper_rel_imf) %>% # mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), # h_geo = fct_relevel(h_geo, h_geos), # geo = fct_relevel(geo, rev(h_geos), after = Inf), # size = !(geo %in% h_geo)) %>% # translate(h_geo, geo_trans) %>% translate(vars, c(nulc_aper = "Indeksi", nulc_aper_rel_imf = "Suhteellinen indeksi")) %>% ggplot(aes(time, values, colour = source)) + facet_wrap(~ vars) + geom_line() + # scale_size_manual(values = c(2,1), guide = "none") + # scale_colour_manual(values = geo_col(length(h_geos) + 1)) + the_title_blank(c("x", "l")) + # scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) + labs(y = "indeksi, 2010 = 100") + the_legend_bot() save_fig("7_nulc_comparison")
OECD EO:n ero tulee siitä, että siinä ei ole laskettu yrittäjäkorjausta.
bind_rows("Eurostat ja OECD QNA" = q_dat, "OECD EO" = eo_q_dat, .id = "source") %>% filter(geo == "FI", time <= max_time_q) %>% select(source, time, nulc_aper, nulc) %>% gather(vars, values, -source, - time) %>% filter(!is.na(values)) %>% unite(vars, vars, source, sep = ", ") %>% ggplot(aes(time, values, colour = vars)) + geom_line() + the_title_blank(c("x", "l")) + labs(y = "indeksi, 2010 = 100") + guides(color = guide_legend(nrow = 2)) + the_legend_bot()
# To check bind_rows("Eurostat ja EO" = q_dat, "OECD ULC" = q_dat_oecd_ulc, .id = "source") %>% select(geo, source, time, nulc_aper, nulc_aper_rel_imf) %>% gather(vars, values, nulc_aper, nulc_aper_rel_imf) %>% # mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), # h_geo = fct_relevel(h_geo, h_geos), # geo = fct_relevel(geo, rev(h_geos), after = Inf), # size = !(geo %in% h_geo)) %>% # translate(h_geo, geo_trans) %>% mutate(source = fct_rev(as_factor(source))) %>% translate(vars, c(nulc_aper = "Indeksi", nulc_aper_rel_imf = "Suhteellinen indeksi")) %>% ggplot(aes(time, values, colour = source)) + facet_grid(geo ~ vars) + geom_line() + # scale_size_manual(values = c(2,1), guide = "none") + # scale_colour_manual(values = geo_col(length(h_geos) + 1)) + the_title_blank(c("x", "l")) + # scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) + labs(y = "indeksi, 2010 = 100")
Koko talous ja teollisuus neljännesvuositiedoissa
Tässä vain eurostat maat
data_main_groups_q %>% select(geo, nace0, time, nulc_aper = nulc_aper_va, nulc_aper_rel_imf = nulc_aper_va_rel_imf) %>% gather(vars, values, nulc_aper, nulc_aper_rel_imf) %>% mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), h_geo = fct_relevel(h_geo, h_geos), geo = fct_relevel(geo, rev(h_geos), after = Inf), size = !(geo %in% h_geo)) %>% translate(h_geo, geo_trans) %>% translate(vars, c(nulc_aper = "Indeksi", nulc_aper_rel_imf = "Suhteellinen indeksi")) %>% translate(nace0, c(total = "Koko talous", manu = "Teollisuus")) %>% ggplot(aes(time, values, group = geo, colour = h_geo, size = size)) + facet_grid(nace0 ~ vars) + geom_line() + scale_size_manual(values = c(2,1), guide = "none") + scale_colour_manual(values = geo_col(length(h_geos))) + the_title_blank(c("x", "l")) + scale_y_continuous(limits = c(70,130), expand = expansion(mult = 0, add = 0)) + labs(y = "indeksi, 2010 = 100") save_fig("8_nulc_q_total_manu")
bind_rows(annual = mutate(data_main_groups_a, time = as.Date(paste0(time, "-07-01"))), quarterly = data_main_groups_q, ilc = mutate(data_ilc, time = as.Date(paste0(time, "-07-01"))), .id = "source") %>% select(geo, time, nace0, source, nulc_va_eur) %>% mutate(nace0 = recode(nace0, manu_ex26 = "manu_ex26_27")) %>% filter(nace0 == "manu") %>% group_by(source) %>% weight_at(geo, time, at = c("nulc_va_eur"), weight_df = weights_ecfin42) %>% ungroup() %>% gather(vars, values, -geo, -time, -source,-nace0) %>% filter(geo == "FI") %>% ggplot(aes(time, values, color = source)) + geom_line() + facet_wrap(~ vars)
bind_rows(annual = mutate(data_main_groups_a, time = as.Date(paste0(time, "-07-01"))), quarterly = data_main_groups_q, ilc = mutate(data_ilc, time = as.Date(paste0(time, "-07-01"))), .id = "source") %>% select(geo, time, nace0, source, nulc_va, lp_ind, d1_per_ind) %>% filter(nace0 == "manu") %>% gather(vars, values, -geo, -time, -source,-nace0) %>% filter(geo == "FI") %>% ggplot(aes(time, values, color = source)) + geom_line() + facet_wrap(~ vars)
data_quartely_manu_est %>% select(geo, time, nace0, nulc_va_eur, nulc_va_eur_long, nulc_va_eur_rel_ecfin21, nulc_va_eur_long_rel_ecfin21) %>% filter(nace0 == "manu") %>% gather(vars, values, -geo, -time, -nace0) %>% filter(geo == "FI") %>% ggplot(aes(time, values, color = vars)) + geom_line()
Different measures quarterly
Different definations for unit labour cost: Output: GDP or value added Relation to hours worked or persons (relevant if adjusted for self-employed or for decomposition)
# lc_dat_nace <- # naq_eurostat_dat %>% # unite(vars, na_item, unit) %>% # # filter(vars %in% c("D1_CP_MNAC", "B1G_CLV10_MNAC")) %>% # spread(vars, values) %>% # group_by(geo, nace_r2) %>% # mutate( # # nominal unit labour cost # nulc_va = ind_ulc(D1_CP_MNAC, B1G_CLV10_MNAC, time = time, baseyear = base_year), # nulc = ind_ulc(D1_CP_MNAC, B1GQ_CLV10_MNAC, time = time, baseyear = base_year), # # nominal unit labour cost, adjusted to take account emploees/emplyed share # nulc_hw_va = ind_ulc(D1_CP_MNAC / SAL_DC_THS_HW, B1G_CLV10_MNAC / EMP_DC_THS_HW, time = time, baseyear = base_year), # nulc_hw = ind_ulc(D1_CP_MNAC / SAL_DC_THS_HW, B1GQ_CLV10_MNAC / EMP_DC_THS_HW, time = time, baseyear = base_year), # nulc_hw_eur = ind_ulc(D1_CP_MEUR / SAL_DC_THS_HW, B1GQ_CLV10_MNAC / EMP_DC_THS_HW, time = time, baseyear = base_year), # nulc_per_va = ind_ulc(D1_CP_MNAC / SAL_DC_THS_PER, B1G_CLV10_MNAC / EMP_DC_THS_PER, time = time, baseyear = base_year), # nulc_per = ind_ulc(D1_CP_MNAC / SAL_DC_THS_PER, B1GQ_CLV10_MNAC / EMP_DC_THS_PER, time = time, baseyear = base_year)) %>% # ungroup() # # lc_dat <- lc_dat_nace %>% # filter(nace_r2 == "TOTAL") q_dat %>% filter(geo %in% h_geos, time >= start_time) %>% select(geo, time, nulc, nulc_va, nulc_hw, nulc_hw_va, nulc_aper, nulc_aper_va) %>% gather(ind, values, -geo, -time) %>% ggplot(aes(time, values, colour = geo)) + geom_line() + facet_wrap(~ ind) + the_title_blank()
On short term a NULC indicator selection does not matter. On longer term differences acculumate.
q_dat %>% filter(geo == "FI", time >= start_time) %>% select(geo, time, nulc, nulc_va, nulc_hw, nulc_hw_va, nulc_aper, nulc_aper_va) %>% gather(ind, values, -geo, -time) %>% group_by(geo, ind) %>% mutate(change = 100 * (values / lag(values, 4, order_by = time) - 1)) %>% ungroup() %>% gather(vars, values, values, change) %>% translate(vars, c(change = "Muutos, %", values = "Taso, indeksi 2010 = 100")) %>% translate(ind, var_labels_fi, simple = TRUE, parts = c(4,5)) %>% ggplot(aes(time, values, colour = ind)) + facet_wrap(~ vars, scales = "free_y") + geom_line() + geom_hline(aes(yintercept = yint), data = data.frame(yint = 0, vars = "Muutos, %")) + the_title_blank() + the_legend_bot() + guides(colour = guide_legend(nrow = 3)) save_fig("9_nulc_ind_comparison")
fast_geos <- data_quartely_est %>% filter(time == max(time) & !is.na(nulc_aper_eur_long)) %>% pull(geo) dat_eff <- ert_eff_ic_q %>% select(geo, time, exch_rt, values) %>% spread(exch_rt, values) data_quartely_est_extra <- data_quartely_est %>% left_join(dat_eff, by = c("geo", "time")) %>% # left_join(data_quartely_manu_est, by = c("geo", "time"), suffix = c("", "_manu")) %>% group_by(time) %>% mutate(nulc_aper_eur_long_rel_ecfin14_extra = weight_index2(nulc_aper_eur_long, geo, time, geos = setdiff(eurostat_geos, c("NL", "EL", "IE")), weight_df = weights_ecfin37)) %>% mutate(nulc_aper_eur_long_rel_fast = weight_index2(nulc_aper_eur_long, geo, time, geos = fast_geos, weight_df = weights_ecfin37)) %>% mutate(rulc_aper_long_rel_fast = weight_index2(rulc_aper_long, geo, time, geos = fast_geos, weight_df = weights_ecfin37)) %>% ungroup() data_quartely_est_extra %>% select(geo, time, # "14 maata" = nulc_aper_eur_long_rel_ecfin14_extra, "20 maata" = nulc_aper_eur_long_rel_ecfin20, "10 maata" = nulc_aper_eur_long_rel_fast, # "EU komission" = REER_IC37_ULCT, "reaalinen, 10 maata" = rulc_aper_long_rel_fast, "reaalinen, 20 maata" = rulc_aper_long_rel_ecfin20,) %>% filter(geo == "FI", time >= start_time) %>% gather(ind, values, -geo, -time) %>% mutate(ind = fct_rev(ind)) %>% mutate(time = halfq_shift(time)) %>% ggplot(aes(time, values, colour = ind)) + geom_line() + geom_hline(yintercept = 100) + the_title_blank(c("x", "l")) + the_legend_bot() + guides(colour = guide_legend()) + labs(title = "Suomen suhteelliset yksikkötyökustannukset samassa valuutassa", subtitle = "eli efektiivinen valuuttakurssi yksikkötyökustannuksilla. Vertailussa komission indikaattori ja aidosta neljännesvuositiededosta lasketut 20 ja 10 maan tietoihin perustuvat indikaattorit", y = "indeksi, 2010 = 100", caption = "Lähde: ECFIN, AMECO, Eurostat, OECD, PTT") # data_quartely_est %>% # select(geo, time, nulc_aper_eur_long) %>% # spread(geo, nulc_aper_eur_long) %>% # View() ggsave_twitter("nulc.png") data_quartely_est_fi <- data_quartely_est %>% filter(geo == "FI") data_quartely_est_extra_fi <- data_quartely_est_extra %>% filter(geo == "FI") library(MacrobondAPI) mb_nulc_aper_eur_long_rel_fast <- MacrobondAPI::CreateTimeSeriesObject(name = "ih:mb:com:nulcefast", description = "Suomen nimelliset suhteelliset yksikkötyökustannukset samassa valuutassa, 10 maata", region = "fi", category = "National accounts", frequency = "Quarterly", startDateOrDates = as.Date(data_quartely_est_extra_fi$time), values = data_quartely_est_extra_fi$nulc_aper_eur_long_rel_fast ) mb_nulc_aper_eur_long_rel_ecfin14 <- MacrobondAPI::CreateTimeSeriesObject(name = "ih:mb:com:nulcecfin14", description = "Suomen nimelliset suhteelliset yksikkötyökustannukset samassa valuutassa, 14 maata", region = "fi", category = "National accounts", frequency = "Quarterly", startDateOrDates = as.Date(data_quartely_est_fi$time), values = data_quartely_est_fi$nulc_aper_eur_long_rel_ecfin14 ) mb_nulc_aper_eur_long_rel_ecfin20 <- MacrobondAPI::CreateTimeSeriesObject(name = "ih:mb:com:nulcecfin20", description = "Suomen nimelliset suhteelliset yksikkötyökustannukset samassa valuutassa, 20 maata", region = "fi", category = "National accounts", frequency = "Quarterly", startDateOrDates = data_quartely_est_fi$time[!is.na(data_quartely_est_fi$nulc_aper_eur_long_rel_ecfin20)], values = data_quartely_est_fi$nulc_aper_eur_long_rel_ecfin20[!is.na(data_quartely_est_fi$nulc_aper_eur_long_rel_ecfin20)] ) mb_rulc_aper_long_rel_fast <- MacrobondAPI::CreateTimeSeriesObject(name = "ih:mb:com:rulcfast", description = "Suomen reaaliset suhteelliset yksikkötyökustannukset, 10 maata", region = "fi", category = "National accounts", frequency = "Quarterly", startDateOrDates = data_quartely_est_extra_fi$time[!is.na(data_quartely_est_extra_fi$rulc_aper_long_rel_fast)], values = data_quartely_est_extra_fi$rulc_aper_long_rel_fast[!is.na(data_quartely_est_extra_fi$rulc_aper_long_rel_fast)] ) UploadOneOrMoreTimeSeries(list(mb_nulc_aper_eur_long_rel_fast, mb_nulc_aper_eur_long_rel_ecfin20, mb_rulc_aper_long_rel_fast))
q_dat %>% select(time, geo, "Palkasaajakorvaukset" = d1_hw_ind, "Yksikkötyökustannukset" = nulc_hw_va_eur, "Työn tuottavuus" = lp_hw_ind) %>% gather(vars, values, -geo, -time) %>% group_by(geo, vars) %>% mutate(values = rebase(values, time, 2015)) %>% ungroup() %>% filter(geo %in% c(geos, "NO", "DK", "NL", "IT", "ES"), time >= "2015-01-01") %>% ggplot(aes(time, values, color = geo)) + facet_wrap(~ vars) + geom_line() + labs(y = "indeksi, 2015, Q1 = 100")
q_dat %>% select(time, geo, d1_hw_ind) %>% gather(vars, values, -geo, -time) %>% group_by(geo, vars) %>% mutate(values = rebase(values, time, 2015)) %>% ungroup() %>% filter(geo %in% c(geos, "NO", "DK", "NL", "IT", "ES"), time >= "2015-01-01") %>% ggplot(aes(time, values, color = geo)) + facet_wrap(~ vars) + geom_line()
Omassa ja samassa valuutassa
Samassa valuutassa volatiliteetti paljon suurempi.
q_dat %>% filter(geo %in% h_geos, time >= start_time) %>% select(geo, time, nulc, nulc_eur) %>% gather(ind, values, -geo, -time, factor_key = TRUE) %>% ggplot(aes(time, values, colour = geo)) + geom_line() + facet_wrap(~ ind) + the_title_blank()
Sama suhteellisina.
q_dat %>% filter(geo %in% h_geos, time >= start_time) %>% select(geo, time, nulc_rel_imf, nulc_eur_rel_imf) %>% gather(ind, values, -geo, -time, factor_key = TRUE) %>% ggplot(aes(time, values, colour = geo)) + geom_line() + facet_wrap(~ ind) + the_title_blank()
# Nominal unit labour cost comparison from different sources # NULC_APER_OECD = NULC_PER_ECB = nulc_per # = (D1_CP_MNAC / SAL_DC_THS_PER) / (B1GQ_CLV10_MNAC / EMP_DC_THS_PER) # # NULC_HW_ECB = nulc_hw # = (D1_CP_MNAC / SAL_DC_THS_HW) / (B1GQ_CLV10_MNAC / EMP_DC_THS_HW) ulc_oecd_with <- ulc_oecd_dat %>% select(- unit) %>% filter(na_item == "NULC_APER") %>% mutate(na_item = paste0(na_item, "_OECD")) %>% group_by(geo, na_item) %>% mutate(values = rebase(values, time, base_year)) %>% ungroup() %>% spread(na_item, values) ulc_eurostat_with <- ulc_eurostat_dat %>% filter(na_item %in% c("NULC_HW", "NULC_PER")) %>% select(time, geo, na_item, values) %>% mutate(na_item = paste0(na_item, "_eurostat")) %>% group_by(geo, na_item) %>% mutate(values = rebase(values, time, base_year)) %>% ungroup() %>% spread(na_item, values) ulc_ecb_with <- ulc_ecb_dat %>% filter(na_item %in% c("NULC_HW", "NULC_PER")) %>% select(time, geo, na_item, values) %>% mutate(na_item = paste0(na_item, "_ECB")) %>% group_by(geo, na_item) %>% mutate(values = rebase(values, time, base_year)) %>% ungroup() %>% spread(na_item, values) lc_with <- lc_dat %>% select(time, geo, nulc, nulc_va, nulc_hw, nulc_hw_va, nulc_per, nulc_per_va) %>% left_join(ulc_oecd_with, by = c("time", "geo")) %>% # left_join(ulc_eurostat_with, by = c("time", "geo")) %>% left_join(ulc_ecb_with, by = c("time", "geo")) # %>% filter(geo == "FI") %>% View() lc_with %>% gather(vars, values, - time, - geo) %>% mutate(vars = fct_relevel(vars, "NULC_APER", after = Inf)) %>% filter(geo == "FI") %>% ggplot(aes(time, values, colour = vars)) + geom_line()
Painotus
Suomelle painoilla ei juuri väliä.
q_dat %>% filter(geo %in% h_geos) %>% select(geo, time, nulc_aper_rel15, nulc_aper_rel_bis, nulc_aper_rel_imf) %>% gather(vars, values, -geo, -time) %>% # mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), # geo = fct_relevel(geo, h_geos, after = Inf)) %>% translate(geo, geo_fi) %>% translate(vars, c(nulc_aper_rel15 = "BIS, kiinteä 2015", nulc_aper_rel_bis = "BIS", nulc_aper_rel_imf = "IMF")) %>% ggplot(aes(time, values, colour = vars)) + geom_line() + facet_wrap(~ geo, scales = "free") + scale_size_manual(values = c(2,1)) + the_title_blank() save_fig("Q_weights")
Vertailu muuttujiin
q_dat %>% filter(geo %in% h_geos) %>% select(geo, time, nulc_aper_rel_imf, gdp_ind_rel_imf, exp_ind_rel_imf, XPERF) %>% mutate(nulc_aper_rel_imf = rebase(1/nulc_aper_rel_imf, time, base_year), XPERF = 100* XPERF) %>% gather(vars, values, -geo, -time) %>% # mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), # geo = fct_relevel(geo, h_geos, after = Inf)) %>% translate(geo, geo_fi) %>% translate(vars, c(nulc_aper_rel_imf = "ULCy, käänteinen", gdp_ind_rel_imf = "BKT", exp_ind_rel_imf = "Vienti", XPERF = "Vientimenestys")) %>% ggplot(aes(time, values, colour = vars)) + geom_line() + facet_wrap(~ geo, scales = "free") + scale_size_manual(values = c(2,1)) + the_title_blank() save_fig("Q_ulc_gdp_comp")
data_quartely_est %>% filter(geo == "FI", time >= "1995-01-01") %>% select(geo, time, nulc_var = nulc_aper_long_rel_ecfin20, gdp_ind_long_rel_ecfin20, exp_ind_long_rel_ecfin20, XPERF) %>% mutate(nulc_var = rebase(1/(nulc_var), time, base_year), XPERF = rebase(XPERF, time, base_year)) %>% gather(vars, values, -geo, -time) %>% # mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), # geo = fct_relevel(geo, h_geos, after = Inf)) %>% translate(geo, geo_fi) %>% # translate(vars, c(nulc_aper_rel_imf = "ULCy, käänteinen", gdp_ind_rel_imf = "BKT", # exp_ind_rel_imf = "Vienti", XPERF = "Vientimenestys")) %>% ggplot(aes(time, values, colour = vars)) + geom_line() + # facet_wrap(~ geo, scales = "free") + scale_size_manual(values = c(2,1)) + scale_y_continuous(sec.axis = sec_axis(~ . * -1)) + the_title_blank() + the_legend_bot() + guides(colour = guide_legend(nrow = 2)) save_board_figs("rel")
data_ameco_long %>% filter(geo == "FI") %>% filter(time >= "1995-01-01") %>% select(geo, time, nulc_var = nulc_aper_rel_ameco15, # rulc_var = rulc_aper_rel_ameco15, out_var = gdp_ind_rel_ameco15) %>% mutate(nulc_var = rebase(1/nulc_var, time, base_year)) %>% # mutate(rulc_var = rebase(1/rulc_var, time, base_year)) %>% # mutate(out_var = 100* (out_var / lag(out_var) -1) + 100) %>% gather(vars, values, -geo, -time) %>% # mutate(h_geo = fct_other(geo, keep = h_geos,other_level = "muut"), # geo = fct_relevel(geo, h_geos, after = Inf)) %>% translate(geo, geo_fi) %>% # translate(vars, c(nulc_aper_rel_imf = "ULCy, käänteinen", gdp_ind_rel_imf = "BKT", # exp_ind_rel_imf = "Vienti", XPERF = "Vientimenestys")) %>% ggplot(aes(time, values, colour = vars)) + geom_line() + facet_wrap(~ geo, scales = "free") + scale_size_manual(values = c(2,1)) + the_title_blank()
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