Bank Lending Survey Statistics - BLS

Data - ECB

Info

source dataset .html .RData

ecb

BLS

2024-09-18 2024-10-08

Data on monetary policy

source dataset .html .RData

bdf

FM

2024-07-26 2024-06-18

bdf

MIR

2024-07-26 2024-07-01

bdf

MIR1

2024-07-26 2024-07-01

bis

CBPOL

2024-08-09 2024-09-15

ecb

BSI

2024-09-18 2024-09-16

ecb

BSI_PUB

2024-09-18 2024-10-08

ecb

FM

2024-09-18 2024-10-08

ecb

ILM

2024-09-18 2024-10-08

ecb

ILM_PUB

2024-09-18 2024-09-10

ecb

liq_daily

2024-09-18 2024-09-11

ecb

MIR

2024-06-19 2024-10-08

ecb

RAI

2024-09-19 2024-10-08

ecb

SUP

2024-09-19 2024-10-08

ecb

YC

2024-09-19 2024-09-16

ecb

YC_PUB

2024-09-19 2024-10-08

eurostat

ei_mfir_m

2024-09-30 2024-09-15

eurostat

irt_st_m

2024-09-30 2024-10-08

fred

r

2024-09-18 2024-09-18

oecd

MEI

2024-04-16 2024-06-30

oecd

MEI_FIN

2024-09-15 2024-05-21

LAST_COMPILE

LAST_COMPILE
2024-10-09

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-Q3 15254
Q 2024-Q2 14798
Q 2024-Q1 14394
Q 2023-Q4 13958

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")