HICP (2015 = 100) - quarterly data (index)

Data - Eurostat

Author

François Geerolf

Info

source dataset Title .html .rData
eurostat prc_hicp_manr HICP (2015 = 100) - monthly data (annual rate of change) 2026-08-13 2026-08-13
eurostat prc_hicp_midx HICP (2015 = 100) - monthly data (index) 2026-08-13 2026-08-13
eurostat prc_hicp_aind HICP (2015 = 100) - annual data (average index and rate of change) 2026-08-13 2026-08-12
eurostat prc_hicp_cow HICP - country weights 2026-08-13 2026-08-12

Data on inflation

source dataset Title .html .rData
eurostat prc_hicp_midx HICP (2015 = 100) - monthly data (index) 2026-08-13 2026-08-13
bis CPI Consumer Price Index 2026-08-12 2026-08-12
ecb CES Consumer Expectations Survey 2026-08-12 2026-08-12
eurostat nama_10_co3_p3 Final consumption expenditure of households by consumption purpose (COICOP 3 digit) 2026-08-13 2026-08-13
eurostat prc_hicp_cow HICP - country weights 2026-08-13 2026-08-12
eurostat prc_hicp_ctrb Contributions to euro area annual inflation (in percentage points) 2026-08-13 2026-08-12
eurostat prc_hicp_inw HICP - item weights 2026-08-13 2026-08-12
eurostat prc_hicp_manr HICP (2015 = 100) - monthly data (annual rate of change) 2026-08-13 2026-08-13
eurostat prc_hicp_mmor HICP (2015 = 100) - monthly data (monthly rate of change) 2026-08-13 2026-08-13
eurostat prc_ppp_ind Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates 2026-08-13 2026-08-12
eurostat sts_inpp_m Producer prices in industry, total - monthly data 2026-08-13 2026-08-12
eurostat sts_inppd_m Producer prices in industry, domestic market - monthly data 2026-08-13 2026-08-12
eurostat sts_inppnd_m Producer prices in industry, non domestic market - monthly data 2026-08-13 2026-08-13
fred cpi Consumer Price Index 2026-08-12 2026-08-12
fred inflation Inflation 2026-08-12 2026-08-12
imf CPI Consumer Price Index (CPI) 2026 February - CPI_2026_FEB_VINTAGE 2026-08-12 2026-08-12
oecd MEI_PRICES_PPI Producer Prices - MEI_PRICES_PPI 2026-08-13 2026-08-02
oecd PPP2017 2017 PPP Benchmark results 2026-08-13 2026-08-02
oecd PRICES_CPI Consumer price indices (CPIs) 2026-08-13 2026-08-02
wdi FP.CPI.TOTL.ZG Inflation, consumer prices (annual %) 2026-08-12 2026-08-12
wdi NY.GDP.DEFL.KD.ZG Inflation, GDP deflator (annual %) 2026-08-12 2026-08-12

LAST_COMPILE

LAST_COMPILE
2026-08-14

Last

Code 
prc_hicp_midx |>
  group_by(time) |>
  summarise(Nobs = n()) |>
  arrange(desc(time)) |>
  head(2) |>
  print_table_conditional()
time Nobs
2025M12 27318
2025M11 27318

unit

Code 
prc_hicp_midx |>
  left_join(unit, by = "unit") |>
  group_by(unit, Unit) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
unit Unit Nobs
I15 Index, 2015=100 3746689
I05 Index, 2005=100 2430233
I96 Index, 1996=100 1545813

coicop

All

Code 
prc_hicp_midx |>
  left_join(coicop, by = "coicop") |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()

2-digit (12 categories)

Code 
prc_hicp_midx |>
  left_join(coicop, by = "coicop") |>
  filter(nchar(coicop) == 4 & substr(coicop, 1, 2) == "CP") |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
coicop Coicop Nobs
CP00 All-items HICP 40745
CP01 Food and non-alcoholic beverages 40745
CP02 Alcoholic beverages, tobacco and narcotics 40745
CP03 Clothing and footwear 40745
CP04 Housing, water, electricity, gas and other fuels 40745
CP05 Furnishings, household equipment and routine household maintenance 40745
CP06 Health 40745
CP07 Transport 40745
CP08 Communications 40745
CP09 Recreation and culture 40745
CP11 Restaurants and hotels 40745
CP12 Miscellaneous goods and services 40745
CP10 Education 40291

3-digit (42 categories)

Code 
prc_hicp_midx |>
  left_join(coicop, by = "coicop") |>
  filter(nchar(coicop) == 5) |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  print_table_conditional()

4-digit (95 categories)

Code 
prc_hicp_midx |>
  left_join(coicop, by = "coicop") |>
  filter(nchar(coicop) == 6 & substr(coicop, 1, 2) == "CP") |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  print_table_conditional()

5-digit (264 categories)

Code 
prc_hicp_midx |>
  left_join(coicop, by = "coicop") |>
  filter(nchar(coicop) == 7 & substr(coicop, 1, 2) == "CP") |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  print_table_conditional()

Non-Coicop

Code 
prc_hicp_midx |>
  left_join(coicop, by = "coicop") |>
  filter(substr(coicop, 1, 2) != "CP") |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()

geo

Code 
prc_hicp_midx |>
  left_join(geo, by = "geo") |>
  group_by(geo, Geo) |>
  summarise(Nobs = n()) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         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 .}

Last

Code 
prc_hicp_midx |>
  filter(time == last_time) |>
  select_if(function(col) length(unique(col)) > 1) |>
  left_join(geo, by = "geo") |>
  left_join(coicop, by = "coicop") |>
  select(geo, Geo, coicop, Coicop, values) |>
  print_table_conditional()

2017T1-

Monthly

Code 
prc_hicp_midx |>
  filter(unit == "I15",
         coicop %in% c("CP00"),
         geo %in% c("DE", "FR", "IT", "EA20", "ES")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  month_to_date() |>
  filter(date >= as.Date("2017-01-01")) |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Augmentation vs. Octobre 2021") +
  scale_x_date(breaks = seq.Date(as.Date("2017-01-01"), Sys.Date(), "6 months"),
               labels = date_format("%b %Y")) +
  scale_y_log10(breaks = seq(0, 200, 5)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Quarterly

Code 
prc_hicp_midx |>
  filter(unit == "I15",
         coicop %in% c("CP00"),
         geo %in% c("DE", "FR", "IT", "EA20", "ES")) |>
  left_join(geo, by = "geo") |>
  select(Geo, time, values) |>
  month_to_date() |>
  filter(date >= as.Date("2017-01-01")) |>
  mutate(date = as.yearqtr(date)) |>
  group_by(Geo, date) |>
  filter(n() == 3) |>
  summarise(values = mean(values)) |>
  ungroup() |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_y_log10(breaks = seq(0, 200, 2),
                labels = percent(seq(0, 200, 2)/100-1)) +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(1, 3)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date == max(date)),
                  aes(x = date, y = values, color = color, label = percent(values/100-1, acc = 0.01)))

2017T2-2024T2

Monthly

Code 
prc_hicp_midx |>
  filter(unit == "I15",
         coicop %in% c("CP00"),
         geo %in% c("DE", "FR", "IT", "NL", "ES")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  month_to_date() |>
  filter(date >= as.Date("2017-04-01")) |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Augmentation vs. Octobre 2021") +
  scale_x_date(breaks = "6 months",
               labels = date_format("%b %Y")) +
  scale_y_log10(breaks = seq(0, 200, 2),
                labels = percent(seq(0, 200, 2)/100-1)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Quarterly

Code 
prc_hicp_midx |>
  filter(unit == "I15",
         coicop %in% c("CP00"),
         geo %in% c("DE", "FR", "IT", "NL", "ES")) |>
  left_join(geo, by = "geo") |>
  select(Geo, time, values) |>
  month_to_date() |>
  mutate(date = as.yearqtr(date)) |>
  filter(date >= as.yearqtr("2017 Q2"),
         date <= as.yearqtr("2024 Q2")) |>
  group_by(Geo, date) |>
  filter(n() == 3) |>
  summarise(values = mean(values)) |>
  ungroup() |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Augmentation vs. Octobre 2021") +
  scale_y_log10(breaks = seq(0, 200, 2),
                labels = percent(seq(0, 200, 2)/100-1)) +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date == max(date)),
                  aes(x = date, y = values, color = color, label = percent(values/100-1, acc = 0.01)))