Economic Outlook No 114 - November 2023 - EO_116

Data - OECD

Author

François Geerolf

Code 
# cd ~/iCloud/website/data; Rscript --vanilla _update_qmd_folder.R oecd only=EO_116.qmd
# sh ~/iCloud/website/website_short.sh
here::i_am("data/oecd/EO_116.qmd")
here() starts at /Users/geerolf/Library/CloudStorage/Dropbox/website
Code 
source(here::here("code", "R-markdown", "init_oecd.R"))

Attachement du package : 'arrow'
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    timestamp
The following packages have been unloaded:
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✔ ggplot2   4.0.3     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
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Please cite as: 


 Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.

 R package version 5.2.3. https://CRAN.R-project.org/package=stargazer 


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Code 
library(arrow)
EO110_INTERNET <- read_parquet("EO110_INTERNET.parquet")
EO111_INTERNET <- read_parquet("EO111_INTERNET.parquet")
EO112_INTERNET <- read_parquet("EO112_INTERNET.parquet")
EO113_INTERNET <- read_parquet("EO113_INTERNET.parquet")
EO114_INTERNET <- read_parquet("EO114_INTERNET.parquet")
EO_115 <- read_parquet("EO_115.parquet")
EO_116 <- read_parquet("EO_116.parquet")
EO_117 <- read_parquet("EO_117.parquet")
load_data("oecd/EO114_INTERNET_var2.RData")
nber_recessions <- read_parquet(here::here("data", "us", "nber_recessions.parquet"))

Info

Last observation: Q: 2026-Q4 (N = 4115) · A: 2026 (N = 9237)

First observation: A: 1960 (N = 1623) · Q: 1960-Q1 (N = 1177)

Last data update: 02 aoû 2026, 08:58. Last compile: 17 aoû 2026, 23:26

Structure

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))