European Central Bank’s API - api

Data - ECB

Author

Info

List of APIs

source	dataset	Title	Updated
bdf	api	Banque de France's API - api	2026-09-09
bea	api	Bureau of Economic Analysis' API - api	2026-09-09
bis	api	Bank of International Settlements' API - api	2026-09-09
bls	api	Bureau of Labor Statistics' API - api	2026-09-08
dbnomics	api	DBnomics' API - api	2026-09-08
ecb	api	European Central Bank's API - api	2026-09-09
eurostat	api	Eurostat's API - api	2026-09-09
imf	api	International Monetary Fund's API - api	2026-09-08
insee	api	Institut National de la Statistique et des Etudes Economiques' API - api	2026-09-08
oecd	api	OECD's API - api	2026-08-02
wdi	api	World Development Indicators' API - api	2026-09-08

LAST_COMPILE

LAST_COMPILE
2026-09-09

Data Structure Definition (DSD)

Code

new <- paste0(base_url, "codelist/all/all/latest?detail=allstubs") |>
  readSDMX() |>
  slot("codelists")


new2 <- paste0(base_url, "datastructure/ECB/ECB_EXR1/1.0?references=children") |>
  readSDMX()

Info

Help for the SDMX protocol: https://sdw-wsrest.ecb.europa.eu/help/
Datasets from the ECB: https://www.ecb.europa.eu/stats/ecb_statistics/escb/html/index.en.html
Statistical Data Warehouse: http://sdw.ecb.europa.eu/browse.do?node=bbn2883

US Dollar / Euro Exchange Rate

Annual

Code

paste0(base_url, "data/EXR/M.USD.EUR.SP00.A") |>
  readSDMX() |>
  as_tibble() |>
  month_to_date() |>
  ggplot() + geom_line(aes(x = date, y = obsValue)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 10, 0.1),
                     labels = dollar_format(accuracy = 0.1, suffix = "/€")) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.80),
        legend.title = element_blank())

Quarterly

Code

paste0(base_url, "data/EXR/Q.USD.EUR.SP00.A") |>
  readSDMX() |>
  as_tibble() |>
  quarter_to_date() |>
  ggplot() + geom_line(aes(x = date, y = obsValue)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 10, 0.1),
                     labels = dollar_format(accuracy = 0.1, suffix = "/€")) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.80),
        legend.title = element_blank())

Financial Markets

Euribor 1-year

Source: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=143.FM.M.U2.EUR.RT.MM.EURIBOR1YD_.HSTA

Code

paste0(base_url, "data/FM/M.U2.EUR.RT.MM.EURIBOR1YD_.HSTA") |> readSDMX() |> as_tibble() |>
  mutate(obsTime = zoo::as.yearmon(obsTime, format = "%Y-%m")) |>
  ggplot() + geom_line(aes(x = obsTime, y = obsValue/100)) +
  xlab("") + ylab("Euribor 1-year") + theme_minimal() +
  zoo::scale_x_yearmon(format = "%Y-%m") +
  theme(legend.position = c(0.65, 0.25),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 0.5),
                     labels = scales::percent_format(accuracy = .1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

Interest rates

Interest rate on minimum reserves

Code

paste0(base_url, "data/BSI/M.U2.N.E.IRR.X.X.U2.0000.Z0Z.I") |> readSDMX() |> as_tibble() |>
  mutate(obsTime = zoo::as.yearmon(obsTime, format = "%Y-%m")) |>
  ggplot() + geom_line(aes(x = obsTime, y = obsValue/100)) +
  xlab("") + ylab("Interest rate on minimum reserves") + theme_minimal() +
  zoo::scale_x_yearmon(format = "%Y-%m") +
  theme(legend.position = c(0.65, 0.25),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 0.5),
                     labels = scales::percent_format(accuracy = .1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

Reserves, required and excess, Current Account

Linear

Code

paste0(base_url, "data/BSI/M.U2.N.R.LRE.X.1.A1.3000.Z01.E") |> readSDMX() |> as_tibble() |>
  bind_rows(paste0(base_url, "data/BSI/M.U2.N.R.LRE.X.1.A1.3000.Z01.E") |> readSDMX() |> as_tibble()) |>
  # ECB retired the monthly U2-aggregate series for this key; only weekly (W)
  # and daily (D) frequencies remain, so we resample weekly to month-end.
  bind_rows(paste0(base_url, "data/ILM/W.U2.C.L020100.U2.EUR") |> readSDMX() |> as_tibble() |>
              mutate(week_start = ISOweek::ISOweek2date(paste0(obsTime, "-1")),
                     obsTime = format(week_start, "%Y-%m")) |>
              arrange(week_start) |>
              group_by(obsTime) |>
              slice_tail(n = 1) |>
              ungroup() |>
              select(-week_start)) |>
  rename(OBS_VALUE = obsValue) |>
  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_continuous(breaks = seq(0, 10000, 500),
                labels = dollar_format(acc = 1, pre = "", su = "Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y"))

Log

Code

paste0(base_url, "data/BSI/M.U2.N.R.LRE.X.1.A1.3000.Z01.E") |> readSDMX() |> as_tibble() |>
  bind_rows(paste0(base_url, "data/BSI/M.U2.N.R.LRE.X.1.A1.3000.Z01.E") |> readSDMX() |> as_tibble()) |>
  # ECB retired the monthly U2-aggregate series for this key; only weekly (W)
  # and daily (D) frequencies remain, so we resample weekly to month-end.
  bind_rows(paste0(base_url, "data/ILM/W.U2.C.L020100.U2.EUR") |> readSDMX() |> as_tibble() |>
              mutate(week_start = ISOweek::ISOweek2date(paste0(obsTime, "-1")),
                     obsTime = format(week_start, "%Y-%m")) |>
              arrange(week_start) |>
              group_by(obsTime) |>
              slice_tail(n = 1) |>
              ungroup() |>
              select(-week_start)) |>
  rename(OBS_VALUE = obsValue) |>
  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, 2100, 2), "-01-01")),
               labels = date_format("%Y"))

Bank Lending Statistics

Loan demand - Weighted net percentage

Code

paste0(base_url, "data/BLS/Q.U2.ALL.Z.H.H+C.B3.ZZ.D.WFNET") |>
  readSDMX() |>
  as_tibble() |>
  mutate(obsTime = zoo::as.yearqtr(obsTime, format = "%Y-Q%q")) |>
  ggplot() + geom_line(aes(x = obsTime, y = obsValue/100, color = BLS_COUNT_DETAIL)) +
  xlab("") + ylab("Loan demand - Weighted net percentage") + theme_minimal() +
  zoo::scale_x_yearqtr(format = "%YQ%q") +
  theme(legend.position = c(0.65, 0.25),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 10),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")