Bank Lending Survey Statistics - BLS

Data - ECB

Author

Info

source	dataset	.html	.RData
ecb	BLS	2024-06-30	2024-07-01

Data on monetary policy

source	dataset	.html	.RData
bdf	FM	2024-07-01	2024-06-18
bdf	MIR	2024-07-01	2024-07-01
bdf	MIR1	2024-07-01	2024-07-01
bis	CBPOL	2024-06-19	2024-07-01
ecb	BSI	2024-06-30	2024-05-21
ecb	BSI_PUB	2024-06-30	2024-07-01
ecb	FM	2024-06-30	2024-07-01
ecb	ILM	2024-06-30	2024-07-01
ecb	ILM_PUB	2024-06-30	2024-01-25
ecb	liq_daily	2024-06-30	2024-05-21
ecb	MIR	2024-06-19	2024-07-01
ecb	RAI	2024-06-07	2024-07-01
ecb	SUP	2024-06-30	2024-07-01
ecb	YC	2024-06-30	2024-05-21
ecb	YC_PUB	2024-06-30	2024-07-01
eurostat	ei_mfir_m	2024-07-01	2024-06-30
eurostat	irt_st_m	2024-07-01	2024-07-01
fred	r	2024-07-01	2024-07-01
oecd	MEI	2024-04-16	2024-06-30
oecd	MEI_FIN	2024-07-01	2024-05-21

LAST_COMPILE

LAST_COMPILE
2024-07-02

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-Q2	14134
Q	2024-Q1	13684
Q	2023-Q4	13328
Q	2023-Q3	13674

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")