Risk Assessment Indicators
Data - ECB
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| ecb | RAI | 2024-05-23 | 2024-06-07 |
- Data Structure Definition. (DSD) html
Data on monetary policy
| source | dataset | .html | .RData |
|---|---|---|---|
| bdf | FM | 2024-05-24 | 2024-06-06 |
| bdf | MIR | 2024-05-24 | 2024-06-06 |
| bdf | MIR1 | 2024-05-24 | 2024-06-06 |
| bis | CBPOL | 2024-05-21 | 2024-06-07 |
| ecb | BSI | 2024-06-07 | 2024-05-21 |
| ecb | BSI_PUB | 2024-06-07 | 2024-06-07 |
| ecb | FM | 2024-06-07 | 2024-06-07 |
| ecb | ILM | 2024-06-07 | 2024-06-07 |
| ecb | ILM_PUB | 2024-06-07 | 2024-01-25 |
| ecb | liq_daily | 2024-06-07 | 2024-05-21 |
| ecb | MIR | 2024-06-07 | 2024-06-07 |
| ecb | RAI | 2024-05-23 | 2024-06-07 |
| ecb | SUP | 2024-05-23 | 2024-06-07 |
| ecb | YC | 2024-05-23 | 2024-05-21 |
| ecb | YC_PUB | 2024-05-23 | 2024-06-07 |
| eurostat | ei_mfir_m | 2024-05-25 | 2024-05-25 |
| eurostat | irt_st_m | 2024-05-25 | 2024-06-07 |
| fred | r | 2024-06-07 | 2024-06-07 |
| oecd | MEI | 2024-04-16 | 2024-04-15 |
| oecd | MEI_FIN | 2024-05-21 | 2024-05-21 |
Data on interest rates
| source | dataset | .html | .RData |
|---|---|---|---|
| bdf | FM | 2024-05-24 | 2024-06-06 |
| bdf | MIR | 2024-05-24 | 2024-06-06 |
| bdf | MIR1 | 2024-05-24 | 2024-06-06 |
| bis | CBPOL_D | 2024-05-06 | 2024-05-10 |
| bis | CBPOL_M | 2024-05-21 | 2024-04-19 |
| ecb | FM | 2024-06-07 | 2024-06-07 |
| ecb | MIR | 2024-06-07 | 2024-06-07 |
| eurostat | ei_mfir_m | 2024-05-25 | 2024-05-25 |
| eurostat | irt_lt_mcby_d | 2024-05-25 | 2024-03-26 |
| eurostat | irt_st_m | 2024-05-25 | 2024-06-07 |
| fred | r | 2024-06-07 | 2024-06-07 |
| oecd | MEI | 2024-04-16 | 2024-04-15 |
| oecd | MEI_FIN | 2024-05-21 | 2024-05-21 |
| wdi | FR.INR.RINR | 2024-01-06 | 2024-04-14 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-06-08 |
Last
| TIME_PERIOD | FREQ | Nobs |
|---|---|---|
| 2024-Q1 | Q | 111 |
| 2024-04 | M | 354 |
DD_ECON_CONCEPT
DD_SUFFIX
RAI %>%
left_join(DD_SUFFIX, by = "DD_SUFFIX") %>%
group_by(DD_SUFFIX, Dd_suffix) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| DD_SUFFIX | Dd_suffix | Nobs |
|---|---|---|
| Z | Not applicable | 100450 |
| P10 | Currency ratio on total currency | 2224 |
| E | Euro | 354 |
SOURCE_DATA
RAI %>%
left_join(SOURCE_DATA, by = "SOURCE_DATA") %>%
group_by(SOURCE_DATA, Source_data) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| SOURCE_DATA | Source_data | Nobs |
|---|---|---|
| BSI | Based on BSI data | 54690 |
| MIR | Based on MIR data | 47910 |
| QSA | Based on quarterly sector accounts data | 344 |
| ICPF | Based on ICPF data | 84 |
DD_SUFFIX
RAI %>%
left_join(DD_SUFFIX, by = "DD_SUFFIX") %>%
group_by(DD_SUFFIX, Dd_suffix) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| DD_SUFFIX | Dd_suffix | Nobs |
|---|---|---|
| Z | Not applicable | 100450 |
| P10 | Currency ratio on total currency | 2224 |
| E | Euro | 354 |
FREQ
RAI %>%
left_join(FREQ, by = "FREQ") %>%
group_by(FREQ, Freq) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| FREQ | Freq | Nobs |
|---|---|---|
| M | Monthly | 92395 |
| Q | Quarterly | 10633 |
REF_AREA
RAI %>%
left_join(REF_AREA, by = "REF_AREA") %>%
group_by(REF_AREA, Ref_area) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Table: Average 2016-2022
RAI %>%
filter(FREQ == "M") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
left_join(DD_ECON_CONCEPT, by = "DD_ECON_CONCEPT") %>%
month_to_date %>%
filter(date >= as.Date("2016-01-01")) %>%
group_by(DD_ECON_CONCEPT, Dd_econ_concept, REF_AREA, Ref_area) %>%
summarise(OBS_VALUE = mean(OBS_VALUE),
Nobs = n()) %>%
print_table_conditional()France
Table
RAI %>%
filter(FREQ == "M",
REF_AREA %in% c("FR", "U2")) %>%
select_if(~ n_distinct(.) > 1) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
group_by(DD_ECON_CONCEPT, Ref_area) %>%
filter(TIME_PERIOD == max(TIME_PERIOD)) %>%
left_join(DD_ECON_CONCEPT, by = "DD_ECON_CONCEPT") %>%
select(Ref_area, DD_ECON_CONCEPT, OBS_VALUE) %>%
spread(Ref_area, OBS_VALUE) %>%
arrange(-`France`) %>%
print_table_conditional()| DD_ECON_CONCEPT | Euro area (Member States and Institutions of the Euro Area) changing composition | France |
|---|---|---|
| SVLHHNFC | 64.36309 | 43.0168281 |
| LC1DHHS | NA | 38.0668202 |
| CT1DGGV | NA | 3.9684428 |
| SVLHPHH | 16.05169 | 3.7633419 |
| LMGLNFC | NA | 0.9783595 |
| LMGBLNFCH | NA | 0.6355577 |
| LMGLHH | NA | -0.3518429 |
| LMGOLNFCH | NA | -0.5156904 |
| GRNLHHNFC | NA | -20.2830536 |
SVLHHNFC, LC1DHHS
RAI %>%
filter(DD_ECON_CONCEPT %in% c("SVLHHNFC", "LC1DHHS"),
REF_AREA %in% c("FR", "U2")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
left_join(DD_ECON_CONCEPT, by = "DD_ECON_CONCEPT") %>%
month_to_date %>%
select_if(~n_distinct(.) > 1) %>%
mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate") +
geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Dd_econ_concept)) +
add_flags(3) + scale_color_identity() +
scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = percent_format(accuracy = 1))
CT1DGGV, SVLHPHH
RAI %>%
filter(DD_ECON_CONCEPT %in% c("CT1DGGV", "SVLHPHH"),
REF_AREA %in% c("FR", "U2")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
left_join(DD_ECON_CONCEPT, by = "DD_ECON_CONCEPT") %>%
month_to_date %>%
select_if(~n_distinct(.) > 1) %>%
mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate") +
geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Dd_econ_concept)) +
add_flags(3) + scale_color_identity() +
scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = percent_format(accuracy = 1))
SVLHPHH - Share of floating rates, Households
Table: Last Time
RAI %>%
filter(DD_ECON_CONCEPT == "SVLHPHH",
TIME_PERIOD %in% c(last_time)) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
select_if(~n_distinct(.) > 1) %>%
select(REF_AREA, Ref_area, OBS_VALUE, OBS_VALUE) %>%
mutate(OBS_VALUE = round(OBS_VALUE, 1)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
arrange(OBS_VALUE) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Table: Many dates
RAI %>%
filter(DD_ECON_CONCEPT == "SVLHPHH",
TIME_PERIOD %in% c(last_time, "2020-01", "2015-01", "2010-01", "2005-01")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
select_if(~n_distinct(.) > 1) %>%
select(REF_AREA, Ref_area, TIME_PERIOD, OBS_VALUE) %>%
mutate(OBS_VALUE = round(OBS_VALUE, 1)) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
print_table_conditional()Netherlands, Germany, Belgium, France, Europe
RAI %>%
filter(DD_ECON_CONCEPT == "SVLHPHH",
REF_AREA %in% c("NL", "DE", "BE", "FR", "U2")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
month_to_date %>%
select_if(~n_distinct(.) > 1) %>%
mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households (%)") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
add_flags(5) + scale_color_identity() +
scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = percent_format(accuracy = 1))
France, Spain, Portugal, latvia, Lithuania, Estonia
RAI %>%
filter(DD_ECON_CONCEPT == "SVLHPHH",
REF_AREA %in% c("FR", "ES", "PT", "EE", "LT", "U2")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
month_to_date %>%
select_if(~n_distinct(.) > 1) %>%
mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households (%)") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
add_flags(6) + scale_color_identity() +
scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = percent_format(accuracy = 1))
France, Spain, Portugal
RAI %>%
filter(DD_ECON_CONCEPT == "SVLHPHH",
REF_AREA %in% c("FR", "ES", "PT")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
month_to_date %>%
select_if(~n_distinct(.) > 1) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households (%)") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
add_flags(3) + scale_color_identity() +
scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = percent_format(accuracy = 1))
Italy, Sweden, Poland
RAI %>%
filter(DD_ECON_CONCEPT == "SVLHPHH",
REF_AREA %in% c("PL", "IT", "SE")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
month_to_date %>%
select_if(~n_distinct(.) > 1) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households (%)") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
add_flags(3) + scale_color_identity() +
scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = percent_format(accuracy = 1))
Netherlands, Germany, Belgium
RAI %>%
filter(DD_ECON_CONCEPT == "SVLHPHH",
REF_AREA %in% c("NL", "DE", "BE")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
month_to_date %>%
select_if(~n_distinct(.) > 1) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households (%)") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
add_flags(3) + scale_color_identity() +
scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = percent_format(accuracy = 1))
SVLHHNFC - Share of floating rates, Households, and Corporations
Table
RAI %>%
filter(DD_ECON_CONCEPT == "SVLHHNFC",
TIME_PERIOD %in% c(last_time, "2020-01", "2015-01", "2010-01", "2005-01")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
select_if(~n_distinct(.) > 1) %>%
select(REF_AREA, Ref_area, TIME_PERIOD, OBS_VALUE) %>%
mutate(OBS_VALUE = round(OBS_VALUE, 1)) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
print_table_conditional()France, Spain, Portugal
RAI %>%
filter(DD_ECON_CONCEPT == "SVLHHNFC",
REF_AREA %in% c("FR", "ES", "PT")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
month_to_date %>%
select_if(~n_distinct(.) > 1) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households and firms (%)") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
add_flags(3) + scale_color_identity() +
scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = percent_format(accuracy = 1))
Italy, Sweden, Poland
RAI %>%
filter(DD_ECON_CONCEPT == "SVLHHNFC",
REF_AREA %in% c("PL", "IT", "SE")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
month_to_date %>%
select_if(~n_distinct(.) > 1) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households and firms (%)") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
add_flags(3) + scale_color_identity() +
scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = percent_format(accuracy = 1))
Netherlands, Germany, Belgium
RAI %>%
filter(DD_ECON_CONCEPT == "SVLHHNFC",
REF_AREA %in% c("NL", "DE", "BE")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
month_to_date %>%
select_if(~n_distinct(.) > 1) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households and firms (%)") +
geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
add_flags(3) + scale_color_identity() +
scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = percent_format(accuracy = 1))