Bank Lending Survey Statistics - BLS
Data - ECB
Info
Data on monetary policy
| source | dataset | .html | .RData |
|---|---|---|---|
| bdf | FM | 2024-12-28 | 2024-12-28 |
| bdf | MIR | 2024-07-26 | 2024-07-01 |
| bdf | MIR1 | 2024-11-29 | 2024-12-09 |
| bis | CBPOL | 2024-12-19 | 2024-12-29 |
| ecb | BSI | 2024-12-28 | 2024-11-19 |
| ecb | BSI_PUB | 2024-12-28 | 2024-12-29 |
| ecb | FM | 2024-12-28 | 2024-12-29 |
| ecb | ILM | 2024-12-28 | 2024-12-29 |
| ecb | ILM_PUB | 2024-12-28 | 2024-09-10 |
| ecb | liq_daily | 2024-12-28 | 2024-09-11 |
| ecb | MIR | 2024-06-19 | 2024-12-29 |
| ecb | RAI | 2024-12-28 | 2024-12-29 |
| ecb | SUP | 2024-12-28 | 2024-12-29 |
| ecb | YC | 2024-12-28 | 2024-11-19 |
| ecb | YC_PUB | 2024-12-28 | 2024-12-29 |
| eurostat | ei_mfir_m | 2024-12-28 | 2024-12-28 |
| eurostat | irt_st_m | 2024-12-28 | 2024-12-29 |
| fred | r | 2024-12-29 | 2024-12-29 |
| oecd | MEI | 2024-04-16 | 2024-06-30 |
| oecd | MEI_FIN | 2024-09-15 | 2024-12-22 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-12-29 |
Last
Code
BLS %>%
group_by(TIME_PERIOD, FREQ) %>%
summarise(Nobs = n()) %>%
ungroup %>%
select(FREQ, TIME_PERIOD, Nobs) %>%
group_by(FREQ) %>%
arrange(desc(TIME_PERIOD)) %>%
filter(row_number() < 5) %>%
arrange(FREQ) %>%
print_table_conditional()| FREQ | TIME_PERIOD | Nobs |
|---|---|---|
| Q | 2024-Q4 | 14798 |
| Q | 2024-Q3 | 15254 |
| Q | 2024-Q2 | 14798 |
| Q | 2024-Q1 | 14394 |
BLS_ITEM
Code
BLS %>%
left_join(BLS_ITEM, by = "BLS_ITEM") %>%
group_by(BLS_ITEM, Bls_item) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Données
Données PCE
Code
BLS %>%
filter(KEY %in% c("BLS.Q.U2.ALL.Z.H.H.B3.ZZ.D.WFNET",
"BLS.Q.U2.ALL.Z.H.C.B3.ZZ.D.WFNET")) %>%
left_join(BLS_COUNT_DETAIL , by = "BLS_COUNT_DETAIL") %>%
quarter_to_date() %>%
select_if(~ n_distinct(.) > 1) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE/100, color = Bls_count_detail)) +
xlab("") + ylab("") + theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2024, 2), "-01-01")),
labels = date_format("%Y")) +
scale_color_manual(values = c("blue", "red")) +
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")
Consumer credit, Loans for house purchase
Code
BLS %>%
filter(KEY %in% c("BLS.Q.U2.ALL.Z.H.H.B3.ZZ.D.WFNET",
"BLS.Q.U2.ALL.Z.H.C.B3.ZZ.D.WFNET")) %>%
left_join(BLS_COUNT_DETAIL , by = "BLS_COUNT_DETAIL") %>%
quarter_to_date() %>%
select_if(~ n_distinct(.) > 1) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE/100, color = Bls_count_detail)) +
xlab("") + ylab("") + theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2024, 2), "-01-01")),
labels = date_format("%Y")) +
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")