Info
ecb |
ILM |
[2024-09-18] |
https://fgee |
olf.com/data |
ecb |
ILM |
Internal Liquidity Management |
2024-10-08 |
[2024-09-18] |
bdf |
FM |
Marché financier, taux |
2024-06-18 |
[2024-07-26] |
bdf |
MIR |
Taux d'intérêt - Zone euro |
2024-07-01 |
[2024-07-26] |
bdf |
MIR1 |
Taux d'intérêt - France |
2024-07-01 |
[2024-07-26] |
bis |
CBPOL |
Policy Rates, Daily |
2024-09-15 |
[2024-08-09] |
ecb |
BSI |
Balance Sheet Items |
2024-09-16 |
[2024-10-08] |
ecb |
BSI_PUB |
Balance Sheet Items - Published series |
2024-10-08 |
[2024-10-08] |
ecb |
FM |
Financial market data |
2024-10-08 |
[2024-10-08] |
ecb |
ILM_PUB |
Internal Liquidity Management - Published series |
2024-09-10 |
[2024-10-08] |
ecb |
MIR |
MFI Interest Rate Statistics |
2024-10-08 |
[2024-06-19] |
ecb |
RAI |
Risk Assessment Indicators |
2024-10-08 |
[2024-09-19] |
ecb |
SUP |
Supervisory Banking Statistics |
2024-10-08 |
[2024-09-19] |
ecb |
YC |
Financial market data - yield curve |
2024-09-16 |
[2024-09-19] |
ecb |
YC_PUB |
Financial market data - yield curve - Published series |
2024-10-08 |
[2024-09-19] |
ecb |
liq_daily |
Daily Liquidity |
2024-09-11 |
[2024-09-18] |
eurostat |
ei_mfir_m |
Interest rates - monthly data |
2024-09-15 |
[2024-09-30] |
eurostat |
irt_st_m |
Money market interest rates - monthly data |
2024-10-08 |
[2024-09-30] |
fred |
r |
Interest Rates |
2024-09-18 |
[2024-09-18] |
oecd |
MEI |
Main Economic Indicators |
2024-06-30 |
[2024-04-16] |
oecd |
MEI_FIN |
Monthly Monetary and Financial Statistics (MEI) |
2024-05-21 |
[2024-09-15] |
Last
Code
ILM %>%
group_by(TIME_PERIOD, FREQ) %>%
summarise(Nobs = n()) %>%
arrange(desc(TIME_PERIOD)) %>%
head(5) %>%
print_table_conditional()
2024-W39 |
W |
35 |
2024-W38 |
W |
35 |
2024-W37 |
W |
35 |
2024-W36 |
W |
35 |
2024-W35 |
W |
35 |
FREQ
Code
ILM %>%
left_join(FREQ, by = "FREQ") %>%
group_by(FREQ, Freq) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}
M |
Monthly |
108492 |
W |
Weekly |
45841 |
D |
Daily |
20813 |
REF_AREA
Code
ILM %>%
left_join(REF_AREA, by = "REF_AREA") %>%
group_by(REF_AREA, Ref_area) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
BS_REP_SECTOR
Code
ILM %>%
left_join(BS_REP_SECTOR, by = "BS_REP_SECTOR") %>%
group_by(BS_REP_SECTOR, Bs_rep_sector) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
BS_ITEM
Code
ILM %>%
left_join(BS_ITEM, by = "BS_ITEM") %>%
group_by(BS_ITEM, Bs_item) %>%
summarise(Nobs = n()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
KEY
Code
ILM %>%
group_by(KEY, TITLE) %>%
summarise(Nobs = n()) %>%
mutate(KEY = paste0('<a target=_blank href=https://data.ecb.europa.eu/data/datasets/ILM/', KEY, ' >', KEY, '</a>')) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}
Daily
List of datasets
Code
ILM %>%
filter(FREQ == "D") %>%
group_by(KEY, TITLE) %>%
summarise(Nobs = n()) %>%
mutate(KEY = paste0("[", KEY, "](https://data.ecb.europa.eu/data/datasets/ILM/", KEY, ')')) %>%
print_table_conditional()
[ILM.D.U2.C.A050500.U2.EUR] |
https://data.ecb.europa.eu/data/datasets/ILM/ILM.D.U2.C. |
050500 |
[ILM.D.U2.C.BMK1.U2.EUR](ht |
ps://data.ecb.europa.eu/data/datasets/ILM/ILM.D.U2.C.BMK |
.U2.EU |
[ILM.D.U2.C.FAAF1.Z5.Z01](h |
tps://data.ecb.europa.eu/data/datasets/ILM/ILM.D.U2.C.FA |
F1.Z5. |
[ILM.D.U2.C.FAAF2.Z5.Z01](h |
tps://data.ecb.europa.eu/data/datasets/ILM/ILM.D.U2.C.FA |
F2.Z5. |
[ILM.D.U2.C.L020100.U2.EUR] |
https://data.ecb.europa.eu/data/datasets/ILM/ILM.D.U2.C. |
020100 |
[ILM.D.U2.C.L020200.U2.EUR] |
https://data.ecb.europa.eu/data/datasets/ILM/ILM.D.U2.C. |
020200 |
Main Refinancing operation
France, Germany, Italy
Code
ILM %>%
filter(KEY %in% c("ILM.M.FR.N.A050100.U2.EUR",
"ILM.M.DE.N.A050100.U2.EUR",
"ILM.M.IT.N.A050100.U2.EUR")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
ylab("") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 10000, 2),
labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 1), "-01-01")),
labels = date_format("%Y"))
Eurosystem
Monthly
Code
ILM %>%
filter(KEY %in% c("ILM.M.U2.C.A050100.U2.EUR")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
ylab("") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 10000, 20),
labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))
Weekly
Code
ILM %>%
filter(KEY %in% c("ILM.W.U2.C.A050100.U2.EUR")) %>%
arrange(desc(TIME_PERIOD)) %>%
week_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE)) +
ylab("Main refinancing operation - Eurosystem") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 10000, 20),
labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))
Total Assets
France, Germany, Italy
Code
ILM %>%
filter(KEY %in% c("ILM.M.DE.N.T000000.Z5.Z01",
"ILM.M.FR.N.T000000.Z5.Z01",
"ILM.M.IT.N.T000000.Z5.Z01")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
ylab("") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 10000, 200),
labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 1), "-01-01")),
labels = date_format("%Y"))
France, Germany, Italy
Code
ILM %>%
filter(KEY %in% c("ILM.M.FR.N.L020000.U2.EUR",
"ILM.M.DE.N.L020000.U2.EUR",
"ILM.M.IT.N.L020000.U2.EUR")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
ylab("") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 10000, 200),
labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 1), "-01-01")),
labels = date_format("%Y"))
Liabilities to euro area credit institutions related to monetary policy operations denominated in euro
Base money
Linear
Code
ILM %>%
filter(KEY %in% c("ILM.M.U2.C.LT00001.Z5.EUR",
"ILM.M.U2.C.L020100.U2.EUR")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
ylab("") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 10000, 500),
labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))
Log
Code
ILM %>%
filter(KEY %in% c("ILM.M.U2.C.LT00001.Z5.EUR",
"ILM.M.U2.C.L020100.U2.EUR")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
ylab("Required and Excess reserves") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = 10^(seq(0, 10, 1)),
labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))
Banknotes in circulation - Differences
Code
ILM %>%
filter(KEY %in% c("ILM.M.4F.E.L010000.Z5.EUR",
"ILM.M.U2.C.L010000.Z5.EUR")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
ylab("Liquidity-providing factors") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 10000, 200),
labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))
Liquidity
https://data.ecb.europa.eu/publications/ecbeurosystem-policy-and-exchange-rates/3030613
Liquidity-providing factors
Individual
Bn€
Code
ILM %>%
filter(KEY %in% c("ILM.M.U2.C.AN00001.Z5.Z0Z",
"ILM.M.U2.C.A050100.U2.EUR",
"ILM.M.U2.C.A050200.U2.EUR",
"ILM.M.U2.C.A050500.U2.EUR",
"ILM.M.U2.C.A050A00.U2.EUR")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
ylab("Liquidity-providing factors
") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 10000, 500),
labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))
Years of GDP
Code
ILM %>%
filter(KEY %in% c("ILM.M.U2.C.AN00001.Z5.Z0Z",
"ILM.M.U2.C.A050100.U2.EUR",
"ILM.M.U2.C.A050200.U2.EUR",
"ILM.M.U2.C.A050500.U2.EUR",
"ILM.M.U2.C.A050A00.U2.EUR")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
left_join(B1GQ %>% mutate(date = date + months(3)), by = "date") %>%
mutate(B1GQ_i = spline(x = date, y = B1GQ, xout = date)$y) %>%
mutate(OBS_VALUE = OBS_VALUE/B1GQ_i) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
ylab("Liquidity-providing factors (years of GDP)") + xlab("") + theme_minimal() +
theme(legend.position = c(0.4, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-5, 100, 5)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))
Stacked
Bn€
Code
ILM %>%
filter(KEY %in% c("ILM.M.U2.C.AN00001.Z5.Z0Z",
"ILM.M.U2.C.A050100.U2.EUR",
"ILM.M.U2.C.A050200.U2.EUR",
"ILM.M.U2.C.A050500.U2.EUR",
"ILM.M.U2.C.A050A00.U2.EUR")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
ggplot + geom_col(aes(x = date, y = OBS_VALUE, fill = TITLE)) +
ylab("Liquidity-providing factors") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 10000, 500),
labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))
Years of GDP
Code
ILM %>%
filter(KEY %in% c("ILM.M.U2.C.AN00001.Z5.Z0Z",
"ILM.M.U2.C.A050100.U2.EUR",
"ILM.M.U2.C.A050200.U2.EUR",
"ILM.M.U2.C.A050500.U2.EUR",
"ILM.M.U2.C.A050A00.U2.EUR")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
left_join(B1GQ %>% mutate(date = date + months(3)), by = "date") %>%
mutate(B1GQ_i = spline(x = date, y = B1GQ, xout = date)$y) %>%
mutate(OBS_VALUE = OBS_VALUE/B1GQ_i) %>%
ggplot + geom_col(aes(x = date, y = OBS_VALUE, fill = TITLE)) +
ylab("Liquidity-providing factors (years of GDP)") + xlab("") + theme_minimal() +
theme(legend.position = c(0.4, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-5, 100, 5)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))
Liquidity-absorbing factors
Individual
Bn€
Code
ILM %>%
filter(KEY %in% c("ILM.M.U2.C.L020200.U2.EUR",
"ILM.M.U2.C.L020300.U2.EUR",
"ILM.M.U2.C.L010000.Z5.EUR",
"ILM.M.U2.C.L050100.U2.EUR",
"ILM.M.U2.C.AN00002.Z5.Z0Z")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
ylab("Liquidity-absorbing factors") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 10000, 500),
labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))
Years of GDP
Code
ILM %>%
filter(KEY %in% c("ILM.M.U2.C.L020200.U2.EUR",
"ILM.M.U2.C.L020300.U2.EUR",
"ILM.M.U2.C.L010000.Z5.EUR",
"ILM.M.U2.C.L050100.U2.EUR",
"ILM.M.U2.C.AN00002.Z5.Z0Z")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
left_join(B1GQ %>% mutate(date = date + months(3)), by = "date") %>%
mutate(B1GQ_i = spline(x = date, y = B1GQ, xout = date)$y) %>%
mutate(OBS_VALUE = OBS_VALUE/B1GQ_i) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
ylab("Liquidity-providing factors (years of GDP)") + xlab("") + theme_minimal() +
theme(legend.position = c(0.4, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-5, 100, 5)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))
Stacked
Bn€
Code
ILM %>%
filter(KEY %in% c("ILM.M.U2.C.L020200.U2.EUR",
"ILM.M.U2.C.L020300.U2.EUR",
"ILM.M.U2.C.L010000.Z5.EUR",
"ILM.M.U2.C.L050100.U2.EUR",
"ILM.M.U2.C.AN00002.Z5.Z0Z")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
ggplot + geom_col(aes(x = date, y = OBS_VALUE, fill = TITLE)) +
ylab("Liquidity-absorbing factors") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 10000, 500),
labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))
Years of GDP
Code
ILM %>%
filter(KEY %in% c("ILM.M.U2.C.L020200.U2.EUR",
"ILM.M.U2.C.L020300.U2.EUR",
"ILM.M.U2.C.L010000.Z5.EUR",
"ILM.M.U2.C.L050100.U2.EUR",
"ILM.M.U2.C.AN00002.Z5.Z0Z")) %>%
month_to_date %>%
mutate(OBS_VALUE = OBS_VALUE/1000) %>%
left_join(B1GQ %>% mutate(date = date + months(3)), by = "date") %>%
mutate(B1GQ_i = spline(x = date, y = B1GQ, xout = date)$y) %>%
mutate(OBS_VALUE = OBS_VALUE/B1GQ_i) %>%
ggplot + geom_col(aes(x = date, y = OBS_VALUE, fill = TITLE)) +
ylab("Liquidity-providing factors (years of GDP)") + xlab("") + theme_minimal() +
theme(legend.position = c(0.4, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-5, 100, 5)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
labels = date_format("%Y"))