source | dataset | .html | .qmd | .RData |
---|---|---|---|---|
ecb | BKN | [2025-08-29] | https://fgee | olf.com/ |
Banknotes statistics
Data - ECB
Info
Data on monetary policy
source | dataset | Title | Download | Compile |
---|---|---|---|---|
bdf | FM | Marché financier, taux | 2025-08-28 | [2025-08-28] |
bdf | MIR | Taux d'intérêt - Zone euro | 2025-08-04 | [2025-08-28] |
bdf | MIR1 | Taux d'intérêt - France | 2025-08-04 | [2025-08-28] |
bis | CBPOL | Policy Rates, Daily | 2025-08-28 | [2025-08-28] |
ecb | BSI | Balance Sheet Items | NA | [2025-08-29] |
ecb | BSI_PUB | Balance Sheet Items - Published series | NA | [2025-08-29] |
ecb | FM | Financial market data | NA | [2025-08-29] |
ecb | ILM | Internal Liquidity Management | NA | [2025-08-29] |
ecb | ILM_PUB | Internal Liquidity Management - Published series | 2024-09-10 | [2025-08-29] |
ecb | MIR | MFI Interest Rate Statistics | 2025-08-28 | [2025-08-29] |
ecb | RAI | Risk Assessment Indicators | 2025-08-28 | [2025-08-29] |
ecb | SUP | Supervisory Banking Statistics | 2025-08-28 | [2025-08-29] |
ecb | YC | Financial market data - yield curve | NA | [2025-08-29] |
ecb | YC_PUB | Financial market data - yield curve - Published series | NA | [2025-08-29] |
ecb | liq_daily | Daily Liquidity | 2025-06-06 | [2025-08-29] |
eurostat | ei_mfir_m | Interest rates - monthly data | 2025-09-26 | [2025-10-01] |
eurostat | irt_st_m | Money market interest rates - monthly data | 2025-09-26 | [2025-10-01] |
fred | r | Interest Rates | 2025-10-01 | [2025-10-02] |
oecd | MEI | Main Economic Indicators | 2025-07-24 | [2024-04-16] |
oecd | MEI_FIN | Monthly Monetary and Financial Statistics (MEI) | 2025-07-24 | [2024-09-15] |
LAST_COMPILE
LAST_COMPILE |
---|
2025-10-09 |
Last
Code
%>%
BKN group_by(TIME_PERIOD, FREQ) %>%
summarise(Nobs = n()) %>%
arrange(desc(TIME_PERIOD)) %>%
head(5) %>%
print_table_conditional()
TIME_PERIOD | FREQ | Nobs |
---|---|---|
2025-07 | M | 303 |
2025-06 | M | 303 |
2025-05 | M | 303 |
2025-04 | M | 303 |
2025-03 | M | 303 |
Info
- Liquidity. html
FREQ
Code
%>%
BKN left_join(FREQ, by = "FREQ") %>%
group_by(FREQ, Freq) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
FREQ | Freq | Nobs |
---|---|---|
M | Monthly | 176926 |
H | Half-yearly | 1574 |
REF_AREA
Code
%>%
BKN 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 .} {
BKN_DENOM
Code
%>%
BKN left_join(BKN_DENOM, by = "BKN_DENOM") %>%
group_by(BKN_DENOM, Bkn_denom) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
BKN_SERIES
Code
%>%
BKN left_join(BKN_SERIES, by = "BKN_SERIES") %>%
group_by(BKN_SERIES, Bkn_series) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
SERIES_DENOM
Code
%>%
BKN left_join(SERIES_DENOM, by = "SERIES_DENOM") %>%
group_by(SERIES_DENOM, Series_denom) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
COLLECTION
Code
%>%
BKN left_join(COLLECTION, by = "COLLECTION") %>%
group_by(COLLECTION, Collection) %>%
summarise(Nobs = n()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
KEY
Code
%>%
BKN group_by(KEY, TITLE) %>%
summarise(Nobs = n()) %>%
mutate(KEY = paste0('<a target=_blank href=https://data.ecb.europa.eu/data/datasets/BKN/', KEY, ' >', KEY, '</a>')) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
Banknotes
Individual
Code
%>%
BKN filter(KEY %in% c("BKN.M.U2.NC10.B.50P2.AS.S.E",
"BKN.M.U2.NC10.B.20P2.AS.S.E",
"BKN.M.U2.NC10.B.10P2.AS.S.E",
"BKN.M.U2.NC10.B.ALLD.AS.S.E")) %>%
%>%
month_to_date mutate(OBS_VALUE = OBS_VALUE/1000000) %>%
left_join(BKN_DENOM, by = "BKN_DENOM") %>%
+ geom_line(aes(x = date, y = OBS_VALUE, color = Bkn_denom)) +
ggplot ylab("Banknotes") + xlab("") + theme_minimal() +
theme(legend.position = c(0.45, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 10000, 100),
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"))
Stacked
Code
%>%
BKN filter(KEY %in% c("BKN.M.U2.NC10.B.50P2.AS.S.E",
"BKN.M.U2.NC10.B.20P2.AS.S.E",
"BKN.M.U2.NC10.B.10P2.AS.S.E")) %>%
%>%
month_to_date mutate(OBS_VALUE = OBS_VALUE/1000000) %>%
+ geom_col(aes(x = date, y = OBS_VALUE, fill = TITLE_COMPL)) +
ggplot 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, 100),
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"))