Economic Outlook No 114 - November 2023

Data - OECD

Code
source("../../code/R-markdown/init_oecd.R")
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Code
load_data("oecd/EO110_INTERNET.RData")
load_data("oecd/EO111_INTERNET.RData")
load_data("oecd/EO112_INTERNET.RData")
load_data("oecd/EO113_INTERNET.RData")
load_data("oecd/EO114_INTERNET.RData")
load_data("oecd/EO_115.RData")
load_data("oecd/EO_116.RData")
load_data("oecd/EO_117.RData")
load_data("oecd/EO114_INTERNET_var2.RData")
load_data("us/nber_recessions.RData")

Info

source dataset .html .RData
oecd EO 2024-06-01 2025-09-29
oecd EO114_INTERNET 2025-08-25 2023-12-08

EO_115

Code
EO_115_extract <- EO_115 %>%
  filter(FREQ == "Q",
         REF_AREA == "DEU",
         MEASURE == "GDPV") %>%
  select(obsTime, obsValue)

EO_116

Code
EO_116_extract <- EO_116 %>%
  filter(FREQ == "Q",
         REF_AREA == "DEU",
         MEASURE == "GDPV") %>%
  select(obsTime, obsValue)

EO_117

Code
EO_117_extract <- EO_117 %>%
  filter(FREQ == "Q",
         REF_AREA == "DEU",
         MEASURE == "GDPV") %>%
  select(obsTime, obsValue)

Germany - Vintages

GDP in Volume

Since 2014Q1

Code
EO110_INTERNET %>%
  mutate(vintage = "Economic Outlook No 110 - December 2021") %>%
  bind_rows(EO111_INTERNET %>%
              mutate(vintage = "Economic Outlook No 111 - June 2022")) %>%
  bind_rows(EO112_INTERNET %>%
              mutate(vintage = "Economic Outlook No 112 - November 2022")) %>%
  bind_rows(EO114_INTERNET %>%
              mutate(vintage = "Economic Outlook No 113 - June 2023")) %>%
  bind_rows(EO114_INTERNET %>%
              mutate(vintage = "Economic Outlook No 114 - November 2023")) %>%
  filter(VARIABLE == "GDPV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") %>%
  bind_rows(EO_115_extract) %>%
  bind_rows(EO_116_extract) %>%
  bind_rows(EO_117_extract) %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2007-01-01")) %>%
  group_by(vintage) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2007-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Since 2014Q1

Code
EO110_INTERNET %>%
  mutate(vintage = "Economic Outlook No 110 - December 2021") %>%
  bind_rows(EO111_INTERNET %>%
              mutate(vintage = "Economic Outlook No 111 - June 2022")) %>%
  bind_rows(EO112_INTERNET %>%
              mutate(vintage = "Economic Outlook No 112 - November 2022")) %>%
  bind_rows(EO114_INTERNET %>%
              mutate(vintage = "Economic Outlook No 113 - June 2023")) %>%
  bind_rows(EO114_INTERNET %>%
              mutate(vintage = "Economic Outlook No 114 - November 2023")) %>%
  filter(VARIABLE == "GDPV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") %>%
  bind_rows(EO_115_extract) %>%
  bind_rows(EO_116_extract) %>%
  bind_rows(EO_117_extract) %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2014-01-01")) %>%
  group_by(vintage) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2014-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Since 2017Q1

Code
EO110_INTERNET %>%
  mutate(vintage = "Economic Outlook No 110 - December 2021") %>%
  bind_rows(EO111_INTERNET %>%
              mutate(vintage = "Economic Outlook No 111 - June 2022")) %>%
  bind_rows(EO112_INTERNET %>%
              mutate(vintage = "Economic Outlook No 112 - November 2022")) %>%
  bind_rows(EO113_INTERNET %>%
              mutate(vintage = "Economic Outlook No 113 - June 2023")) %>%
  bind_rows(EO114_INTERNET %>%
              mutate(vintage = "Economic Outlook No 114 - November 2023")) %>%
  filter(VARIABLE == "GDPV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") %>%
  bind_rows(EO_115_extract) %>%
  bind_rows(EO_116_extract) %>%
  bind_rows(EO_117_extract) %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2017-01-01")) %>%
  group_by(vintage) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2017-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Since 2019Q4

Code
EO110_INTERNET %>%
  mutate(vintage = "Economic Outlook No 110 - December 2021") %>%
  bind_rows(EO111_INTERNET %>%
              mutate(vintage = "Economic Outlook No 111 - June 2022")) %>%
  bind_rows(EO112_INTERNET %>%
              mutate(vintage = "Economic Outlook No 112 - November 2022")) %>%
  bind_rows(EO113_INTERNET %>%
              mutate(vintage = "Economic Outlook No 113 - June 2023")) %>%
  bind_rows(EO114_INTERNET %>%
              mutate(vintage = "Economic Outlook No 114 - November 2023")) %>%
  filter(VARIABLE == "GDPV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") %>%
  bind_rows(EO_115_extract) %>%
  bind_rows(EO_116_extract) %>%
  bind_rows(EO_117_extract) %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2019-10-01")) %>%
  group_by(vintage) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2019-10-01")]) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Since 2021

Code
EO110_INTERNET %>%
  mutate(vintage = "Economic Outlook No 110 - December 2021") %>%
  bind_rows(EO111_INTERNET %>%
              mutate(vintage = "Economic Outlook No 111 - June 2022")) %>%
  bind_rows(EO112_INTERNET %>%
              mutate(vintage = "Economic Outlook No 112 - November 2022")) %>%
  bind_rows(EO113_INTERNET %>%
              mutate(vintage = "Economic Outlook No 113 - June 2023")) %>%
  bind_rows(EO114_INTERNET %>%
              mutate(vintage = "Economic Outlook No 114 - November 2023")) %>%
  filter(VARIABLE == "GDPV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") %>%
  bind_rows(EO_115_extract %>%
              mutate(vintage = "Economic Outlook No 115 - May 2024")) %>%
  bind_rows(EO_116_extract %>%
              mutate(vintage = "Economic Outlook No 116 - December 2024")) %>%
  bind_rows(EO_117_extract %>%
              mutate(vintage = "Economic Outlook No 117 - June 2025")) %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2021-01-01")) %>%
  group_by(vintage) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2021-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

French

Code
EO110_INTERNET %>%
  mutate(vintage = "Perspectives économiques No 110 - Décembre 2021") %>%
  bind_rows(EO111_INTERNET %>%
              mutate(vintage = "Perspectives économiques No 111 - June 2022")) %>%
  bind_rows(EO112_INTERNET %>%
              mutate(vintage = "Perspectives économiques No 112 - November 2022")) %>%
  bind_rows(EO113_INTERNET %>%
              mutate(vintage = "Perspectives économiques No 113 - June 2023")) %>%
  bind_rows(EO114_INTERNET %>%
              mutate(vintage = "Perspectives économiques No 114 - November 2023")) %>%
  filter(VARIABLE == "GDPV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") %>%
  bind_rows(EO_115_extract %>%
              mutate(vintage = "Perspectives économiques No 115 - May 2024")) %>%
  bind_rows(EO_116_extract %>%
              mutate(vintage = "Perspectives économiques No 116 - December 2024")) %>%
  bind_rows(EO_117_extract %>%
              mutate(vintage = "Perspectives économiques No 117 - June 2025")) %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2021-01-01")) %>%
  group_by(vintage) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2021-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

French

Code
EO110_INTERNET %>%
  mutate(vintage = "Perspectives économiques No 110 - Décembre 2021") %>%
  filter(VARIABLE == "GDPV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") %>%
  bind_rows(EO_117_extract %>%
              mutate(vintage = "Perspectives économiques No 117 - Juin 2025")) %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2021-01-01")) %>%
  bind_rows(EO_117_extract %>%
              mutate(vintage = "PIB réalisé") %>%
              quarter_to_date() %>%
              filter(date <= as.Date("2024-10-01"),
                     date >= as.Date("2021-01-01"))) %>%
  group_by(vintage) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2021-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, linetype = vintage)) +
  xlab("") + ylab("PIB en volume, Allemagne") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

U.S. Since 2021

Code
EO110_INTERNET %>%
  mutate(vintage = "Economic Outlook No 110 - December 2021") %>%
  bind_rows(EO111_INTERNET %>%
              mutate(vintage = "Economic Outlook No 111 - June 2022")) %>%
  bind_rows(EO112_INTERNET %>%
              mutate(vintage = "Economic Outlook No 112 - November 2022")) %>%
  bind_rows(EO113_INTERNET %>%
              mutate(vintage = "Economic Outlook No 113 - June 2023")) %>%
  bind_rows(EO114_INTERNET %>%
              mutate(vintage = "Economic Outlook No 114 - November 2023")) %>%
  filter(VARIABLE == "GDPV", 
         LOCATION == "USA",
         FREQUENCY == "Q") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2021-01-01")) %>%
  group_by(vintage) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2021-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, United States") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Consumption in Volume

Code
EO110_INTERNET %>%
  mutate(vintage = "Economic Outlook No 110 - December 2021") %>%
  bind_rows(EO111_INTERNET %>%
              mutate(vintage = "Economic Outlook No 111 - June 2022")) %>%
  bind_rows(EO112_INTERNET %>%
              mutate(vintage = "Economic Outlook No 112 - November 2022")) %>%
  bind_rows(EO113_INTERNET %>%
              mutate(vintage = "Economic Outlook No 113 - June 2023")) %>%
  bind_rows(EO114_INTERNET %>%
              mutate(vintage = "Economic Outlook No 114 - November 2023")) %>%
  filter(VARIABLE == "CPV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2021-01-01")) %>%
  group_by(vintage) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2021-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Consumption in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Investment in Volume

Code
EO110_INTERNET %>%
  mutate(vintage = "Economic Outlook No 110 - December 2021") %>%
  bind_rows(EO111_INTERNET %>%
              mutate(vintage = "Economic Outlook No 111 - June 2022")) %>%
  bind_rows(EO112_INTERNET %>%
              mutate(vintage = "Economic Outlook No 112 - November 2022")) %>%
  bind_rows(EO113_INTERNET %>%
              mutate(vintage = "Economic Outlook No 113 - June 2023")) %>%
  bind_rows(EO114_INTERNET %>%
              mutate(vintage = "Economic Outlook No 114 - November 2023")) %>%
  filter(VARIABLE == "ITV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2021-01-01")) %>%
  group_by(vintage) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2021-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Investment in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))