Table 4.1. Foreign Transactions in the National Income and Product Accounts - T40100

Data - BEA

Author

François Geerolf

Last observation: Q4 2024 (N = 18) · 2024 (N = 36)

First observation: 1929 (N = 21) · Q1 1947 (N = 21)

Last data update: 25 juil. 2026, 12:36

Last compile: 05 sept. 2026, 22:42

Layout

  • NIPA Website. html

LineNumber, LineDescription

Code 
T40100_A |>
  group_by(LineNumber, LineDescription) |>
  summarise(Nobs = n())
# # A tibble: 36 × 3
# # Groups:   LineNumber [36]
#    LineNumber LineDescription                              Nobs
#         <dbl> <chr>                                       <int>
#  1          1 Current receipts from the rest of the world    96
#  2          2 Exports of goods and services                  96
#  3          3 Goods                                          96
#  4          4 Durable                                        96
#  5          5 Nondurable                                     96
#  6          6 Services                                       96
#  7          7 Income receipts                                96
#  8          8 Wage and salary receipts                       77
#  9          9 Income receipts on assets                      77
# 10         10 Interest                                       79
# # ℹ 26 more rows

Imports and Exports

Graph, Annual

Code 
T40100_A |>
  year_to_date() |>
  filter(LineNumber %in% c(1, 16, 19)) |>
  group_by(date) |>
  filter(n() == 3) |>
  transmute(date,
            `Exports` = DataValue[LineNumber == 16]/DataValue[LineNumber == 1],
            `Imports` = DataValue[LineNumber == 19]/DataValue[LineNumber == 1]) |>
  gather(variable, value, -date) |>
  ggplot() + theme_minimal() + xlab("") +  ylab("Exports, Imports (% of GDP)") +
  geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.9)) +
  scale_y_continuous(breaks = seq(-0.06, 0.4, 0.02),
                     labels = scales::percent_format(accuracy = 1))

Goods, Services

Code 
T40100_A |>
  year_to_date() |>
  filter(LineNumber %in% c(1, 17, 18, 20, 21)) |>
  group_by(date) |>
  filter(n() == 5) |>
  transmute(date,
            `Exports - Goods` = DataValue[LineNumber == 17]/DataValue[LineNumber == 1],
            `Exports - Services` = DataValue[LineNumber == 18]/DataValue[LineNumber == 1],
            `Imports - Goods` = DataValue[LineNumber == 20]/DataValue[LineNumber == 1],
            `Imports - Services` = DataValue[LineNumber == 21]/DataValue[LineNumber == 1]) |>
  gather(variable, value, -date) |>
  ggplot() + theme_minimal() + xlab("") +  ylab("Exports, Imports (% of GDP)") +
  geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_color_manual(values = c(viridis(3)[1], viridis(3)[2], viridis(3)[1], viridis(3)[2])) +
  scale_linetype_manual(values = c("dashed", "dashed", "solid",  "solid")) +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.9)) +
  scale_y_continuous(breaks = seq(-0.06, 0.4, 0.02),
                     labels = scales::percent_format(accuracy = 1))

Goods, Services with NBER Recessions

Code 
T40100_A |>
  year_to_date() |>
  filter(LineNumber %in% c(1, 17, 18, 20, 21)) |>
  group_by(date) |>
  filter(n() == 5) |>
  transmute(date,
            `Exports - Goods` = DataValue[LineNumber == 17]/DataValue[LineNumber == 1],
            `Exports - Services` = DataValue[LineNumber == 18]/DataValue[LineNumber == 1],
            `Imports - Goods` = DataValue[LineNumber == 20]/DataValue[LineNumber == 1],
            `Imports - Services` = DataValue[LineNumber == 21]/DataValue[LineNumber == 1]) |>
  gather(variable, value, -date) |>
  ggplot() + theme_minimal() + xlab("") +  ylab("Exports, Imports (% of GDP)") +
  geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_color_manual(values = c(viridis(3)[1], viridis(3)[2], viridis(3)[1], viridis(3)[2])) +
  scale_linetype_manual(values = c("dashed", "dashed", "solid",  "solid")) +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.9)) +
  scale_y_continuous(breaks = seq(-0.06, 0.4, 0.02),
                     labels = scales::percent_format(accuracy = 1))

Net Exports

Quarterly

Code 
T40100_Q |>
  quarter_to_date() |>
  filter(LineNumber %in% c(1, 15)) |>
  group_by(date) |>
  filter(n() == 2) |>
  mutate(net_exports_gdp = DataValue[LineNumber == 15]/DataValue[LineNumber == 1]) |>
  ggplot() + theme_minimal() + xlab("") +  ylab("Net Exports (% of GDP)") +
  geom_line(aes(x = date, y = net_exports_gdp)) +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.8)) +
  scale_y_continuous(breaks = seq(-0.06, 0.05, 0.01),
                     labels = scales::percent_format(accuracy = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = viridis(3)[2])

Annual

Code 
T40100_A |>
  year_to_date() |>
  filter(LineNumber %in% c(1, 15)) |>
  group_by(date) |>
  filter(n() == 2) |>
  mutate(net_exports_gdp = DataValue[LineNumber == 15]/DataValue[LineNumber == 1]) |>
  ggplot() + theme_minimal() + xlab("") +  ylab("Net Exports (% of GDP)") +
  geom_line(aes(x = date, y = net_exports_gdp)) +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.8)) +
  scale_y_continuous(breaks = seq(-0.06, 0.05, 0.01),
                     labels = scales::percent_format(accuracy = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = viridis(3)[2])

Graph with Presidents

Code 
US_presidents_extract <- US_presidents |>
  filter(start >= as.Date("1945-01-01"))
T40100_Q |>
  quarter_to_date() |>
  filter(LineNumber %in% c(1, 15)) |>
  group_by(date) |>
  filter(n() == 2) |>
  mutate(net_exports_gdp = DataValue[LineNumber == 15]/DataValue[LineNumber == 1]) |>
  ggplot() + theme_minimal() + xlab("") +  ylab("Net Exports (% of GDP)") +
  geom_line(aes(x = date, y = net_exports_gdp)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.8)) +
  scale_x_date(breaks = as.Date(US_presidents_extract$start),
               labels = date_format("%Y")) +
  geom_vline(aes(xintercept = as.numeric(start)), 
             data = US_presidents,
             colour = "grey50", alpha = 0.5) +
  geom_text(aes(x = start, y = new, label = name), 
            data = US_presidents_extract |> mutate(new = -0.08 + 0.005 * (1:n() %% 2)),
            size = 2.5, vjust = 0, hjust = 0, nudge_x = 50) +
  geom_rect(aes(xmin = start, xmax = end, fill = party),
         ymin = -Inf, ymax = Inf, alpha = 0.1, data = US_presidents_extract) +
  scale_fill_manual(values = c("white", "grey")) +
  guides(color = guide_legend("party"), fill = FALSE) +
  scale_y_continuous(breaks = seq(-0.06, 0.05, 0.01),
                     labels = scales::percent_format(accuracy = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = viridis(3)[2])

Data

Code 
T40100_Q |>
  quarter_to_date() |>
  filter(LineNumber %in% c(1, 15),
         date >= as.Date("2016-12-01")) |>
  select(LineNumber, date, DataValue) |>
  spread(LineNumber, DataValue) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Data - Plots

Code 
T40100_Q |>
  quarter_to_date() |>
  filter(LineNumber %in% c(1, 15),
         date >= as.Date("2017-01-01")) |>
  select(LineNumber, date, DataValue) |>
  spread(LineNumber, DataValue) |>
  ggplot() + geom_line(aes(x = date, y = `15`/1000)) +
  theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2024, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = seq(-2000, 2000, 100),
                labels = dollar_format(suffix = " Bn", accuracy = 1))

1938, 1958, 1978, 1998, 2018 Table

Percent

Code 
T40100_A |>
  year_to_date() |>
  mutate(year = year(date)) |>
  filter(year %in% c(1929, 1949, 1969, 1989, 2009, 2019)) |>
  group_by(year) |>
  mutate(value = round(100*DataValue/DataValue[1], 1)) |>
  ungroup() |>
  select(2, 3, 6, 7) |>
  spread(year, value) %>%
  mutate_at(vars(-1, -2), funs(ifelse(is.na(.), "", paste0(., " %")))) |>
  select(-LineNumber) %>%
  setNames(c("", names(.)[2:7])) |>
  knitr::kable(booktabs = TRUE,
               linesep = "") |>
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                latex_options = c("striped", "hold_position")) |>
  add_indent(c(2:26)) |>
  add_indent(c(3:6, 8:14, 16:21, 23:26)) |>
  add_indent(c(4:5, 9:13, 17:18, 20:21, 24:25)) |>
  add_indent(c(10:12))
1929 1949 1969 1989 2009 2019
Current receipts from the rest of the world 100 % 100 % 100 % 100 % 100 % 100 %
Exports of goods and services 83.9 % 88.2 % 81.5 % 73.9 % 67 % 65.5 %
Goods 75.5 % 74.3 % 60.8 % 54.9 % 44.8 % 42.4 %
Durable 31.2 % 37.1 % 37.6 % 35.6 % 28.3 % 25.4 %
Nondurable 44.3 % 37.3 % 23.3 % 19.4 % 16.5 % 17 %
Services 8.4 % 13.8 % 20.7 % 19 % 22.2 % 23.1 %
Income receipts 16.1 % 11.8 % 18.5 % 26.1 % 29.2 % 30.3 %
Wage and salary receipts 0.7 % 0.4 % 0.1 % 0.2 % 0.2 %
Income receipts on assets 11.1 % 18.1 % 25.9 % 28.9 % 30.1 %
Interest 1.7 % 5.5 % 14.8 % 7.9 % 7.3 %
Dividends 6.8 % 7.6 % 7 % 10 % 18.2 %
Reinvested earnings on U.S. direct investment abroad 2.7 % 5 % 4.1 % 11 % 4.6 %
Current taxes, contributions for government social insurance, and transfer receipts from the rest of the world 3.8 % 4.2 %
To persons 0.2 % 0.2 %
To business 2.7 % 2.9 %
To government 0.9 % 1 %
Current payments to the rest of the world 89.1 % 94.6 % 97.5 % 113.5 % 116.2 % 111.5 %
Imports of goods and services 78.5 % 56.3 % 79.3 % 86.7 % 84.8 % 80.4 %
Goods 63 % 41.8 % 57.8 % 71.1 % 67.2 % 64.9 %
Durable 17.7 % 12.9 % 32.6 % 45.5 % 37.8 % 42 %
Nondurable 45.4 % 28.9 % 25.2 % 25.6 % 29.4 % 22.9 %
Services 15.4 % 14.5 % 21.5 % 15.6 % 17.5 % 15.5 %
Income payments 5.3 % 4 % 8.9 % 22.4 % 22.8 % 23 %
Wage and salary payments 0.3 % 0.3 % 0.3 % 0.6 % 0.5 %
Income payments on assets 3.7 % 8.6 % 22.1 % 22.2 % 22.5 %
Interest 1.1 % 6.3 % 20.9 % 16 % 13.5 %
Dividends 1.7 % 1.6 % 2.4 % 5 % 5.8 %
Reinvested earnings on foreign direct investment in the United States 0.9 % 0.7 % -1.2 % 1.2 % 3.2 %
Current taxes and transfer payments to the rest of the world 5.3 % 34.3 % 9.3 % 4.5 % 8.6 % 8.1 %
From persons 4.8 % 3.1 % 1.8 % 1.7 % 3 % 2.6 %
From government 0.5 % 31.2 % 7.1 % 2.1 % 2.7 % 1.9 %
From business 0 % 0 % 0.5 % 0.7 % 3 % 3.5 %
Balance on current account, NIPAs 10.9 % 5.4 % 2.5 % -13.5 % -16.2 % -11.5 %
Net lending or net borrowing (-), NIPAs 10.9 % 5.4 % 2.5 % -13.6 % -16.5 % -11.7 %
Balance on current account, NIPAs 10.9 % 5.4 % 2.5 % -13.5 % -16.2 % -11.5 %
Less: Capital account transactions (net) 0 % 0.1 % 0.3 % 0.2 %

Percent - pdf

Code 
include_graphics2("https://fgeerolf.com/bib/bea/T40100_ex.png")

Billions

Code 
T40100_A |>
  year_to_date() |>
  mutate(year = year(date)) |>
  filter(year %in% c(1929, 1949, 1969, 1989, 2009, 2019)) |>
  group_by(year) |>
  mutate(value = round(DataValue/1000)) |>
  ungroup() |>
  select(2, 3, 6, 7) |>
  spread(year, value) %>%
  mutate_at(vars(-1, -2), funs(ifelse(is.na(.), "", paste0("$ ",., " Bn")))) |>
  select(-LineNumber) %>%
  setNames(c("", names(.)[2:7])) |>
  knitr::kable(booktabs = TRUE,
               linesep = "") |>
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                latex_options = c("striped", "hold_position")) |>
  add_indent(c(2:26)) |>
  add_indent(c(3:6, 8:14, 16:21, 23:26)) |>
  add_indent(c(4:5, 9:13, 17:18, 20:21, 24:25)) |>
  add_indent(c(10:12))
1929 1949 1969 1989 2009 2019
Current receipts from the rest of the world $ 7 Bn $ 16 Bn $ 64 Bn $ 682 Bn $ 2362 Bn $ 3876 Bn
Exports of goods and services $ 6 Bn $ 14 Bn $ 52 Bn $ 504 Bn $ 1583 Bn $ 2539 Bn
Goods $ 5 Bn $ 12 Bn $ 39 Bn $ 375 Bn $ 1057 Bn $ 1645 Bn
Durable $ 2 Bn $ 6 Bn $ 24 Bn $ 243 Bn $ 668 Bn $ 985 Bn
Nondurable $ 3 Bn $ 6 Bn $ 15 Bn $ 132 Bn $ 390 Bn $ 659 Bn
Services $ 1 Bn $ 2 Bn $ 13 Bn $ 130 Bn $ 525 Bn $ 895 Bn
Income receipts $ 1 Bn $ 2 Bn $ 12 Bn $ 178 Bn $ 689 Bn $ 1175 Bn
Wage and salary receipts $ 0 Bn $ 0 Bn $ 1 Bn $ 6 Bn $ 7 Bn
Income receipts on assets $ 2 Bn $ 12 Bn $ 177 Bn $ 684 Bn $ 1168 Bn
Interest $ 0 Bn $ 3 Bn $ 101 Bn $ 187 Bn $ 284 Bn
Dividends $ 1 Bn $ 5 Bn $ 48 Bn $ 237 Bn $ 706 Bn
Reinvested earnings on U.S. direct investment abroad $ 0 Bn $ 3 Bn $ 28 Bn $ 260 Bn $ 178 Bn
Current taxes, contributions for government social insurance, and transfer receipts from the rest of the world $ 90 Bn $ 162 Bn
To persons $ 6 Bn $ 9 Bn
To business $ 64 Bn $ 112 Bn
To government $ 21 Bn $ 40 Bn
Current payments to the rest of the world $ 6 Bn $ 16 Bn $ 62 Bn $ 774 Bn $ 2745 Bn $ 4323 Bn
Imports of goods and services $ 6 Bn $ 9 Bn $ 50 Bn $ 591 Bn $ 2002 Bn $ 3117 Bn
Goods $ 4 Bn $ 7 Bn $ 37 Bn $ 485 Bn $ 1588 Bn $ 2517 Bn
Durable $ 1 Bn $ 2 Bn $ 21 Bn $ 310 Bn $ 893 Bn $ 1628 Bn
Nondurable $ 3 Bn $ 5 Bn $ 16 Bn $ 175 Bn $ 695 Bn $ 888 Bn
Services $ 1 Bn $ 2 Bn $ 14 Bn $ 106 Bn $ 414 Bn $ 600 Bn
Income payments $ 0 Bn $ 1 Bn $ 6 Bn $ 153 Bn $ 539 Bn $ 893 Bn
Wage and salary payments $ 0 Bn $ 0 Bn $ 2 Bn $ 14 Bn $ 19 Bn
Income payments on assets $ 1 Bn $ 5 Bn $ 151 Bn $ 525 Bn $ 874 Bn
Interest $ 0 Bn $ 4 Bn $ 142 Bn $ 377 Bn $ 523 Bn
Dividends $ 0 Bn $ 1 Bn $ 16 Bn $ 119 Bn $ 227 Bn
Reinvested earnings on foreign direct investment in the United States $ 0 Bn $ 0 Bn $ -8 Bn $ 29 Bn $ 124 Bn
Current taxes and transfer payments to the rest of the world $ 0 Bn $ 6 Bn $ 6 Bn $ 30 Bn $ 204 Bn $ 314 Bn
From persons $ 0 Bn $ 1 Bn $ 1 Bn $ 12 Bn $ 70 Bn $ 102 Bn
From government $ 0 Bn $ 5 Bn $ 5 Bn $ 14 Bn $ 63 Bn $ 74 Bn
From business $ 0 Bn $ 0 Bn $ 0 Bn $ 5 Bn $ 72 Bn $ 137 Bn
Balance on current account, NIPAs $ 1 Bn $ 1 Bn $ 2 Bn $ -92 Bn $ -383 Bn $ -447 Bn
Net lending or net borrowing (-), NIPAs $ 1 Bn $ 1 Bn $ 2 Bn $ -93 Bn $ -389 Bn $ -454 Bn
Balance on current account, NIPAs $ 1 Bn $ 1 Bn $ 2 Bn $ -92 Bn $ -383 Bn $ -447 Bn
Less: Capital account transactions (net) $ 0 Bn $ 0 Bn $ 6 Bn $ 7 Bn

Econ 102

U.S. GDP and Consumption (1929-2019)

Linear

Code 
plot_linear <- T40100_A |>
  year_to_date() |>
  filter(LineNumber %in% c(1, 2)) |>
  ggplot() + xlab("") + ylab("U.S. GDP") +
  geom_line(aes(x = date, y = DataValue/1000000, color = LineDescription)) + 
  theme_minimal() +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 1*seq(0, 40, 2.5),
                     labels = scales::dollar_format(accuracy = 0.1, suffix = "Tn"))

plot_linear

Log

Code 
plot_log <- plot_linear +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_y_log10(breaks = c(0.1, 0.2, 0.5, 1, 2, 5, 8, 10, 20),
                     labels = scales::dollar_format(accuracy = 0.1, suffix = "Tn"))

plot_log

Both

Code 
ggpubr::ggarrange(plot_linear + ggtitle("Linear Scale"),
                  plot_log + ggtitle("Log Scale") + ylab(""),
                  common.legend = T)

U.S. GDP (1929-2019)

(ref:gdp-only) U.S. GDP (1929-2019).

Code 
T40100_A |>
  year_to_date() |>
  filter(LineNumber == 1) |>
  ggplot() + xlab("") + ylab("U.S. GDP") +
  geom_line(aes(x = date, y = DataValue/1000000)) + 
  theme_minimal() +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 1*seq(0, 40, 2.5),
                     labels = scales::dollar_format(accuracy = 0.1, suffix = "Tn"))

(ref:gdp-only)

U.S. GDP (1929-2019) - Log Scale

(ref:log-gdp-only) U.S. GDP (1929-2019) - Log Scale.

Code 
T40100_A |>
  year_to_date() |>
  filter(LineNumber == 1) |>
  ggplot() + xlab("") + ylab("U.S. GDP (Log Scale)") +
  geom_line(aes(x = date, y = DataValue/1000000)) + theme_minimal() +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = 20*2^seq(-9, 1, 1),
                labels = scales::dollar_format(accuracy = 0.1, suffix = "Tn"))

(ref:log-gdp-only)

Government Purchases

HP Filter

(ref:g-purchases) Government Purchases.

Code 
T40100_A |>
  year_to_date() |>
  filter(LineNumber %in% c(1, 22)) |>
  group_by(date) |>
  summarise(government_gdp = DataValue[LineNumber == 22]/DataValue[LineNumber == 1]) |>
  select(date, government_gdp) |>
  inner_join(gdp_real_A, by = "date") |>
  mutate(year = year(date),
         gdp_real_A_hp10000 = exp(hpfilter(log(gdp_real_A), freq = 10000)$trend),
         gdp_real_A_LLtrend = lm(log(gdp_real_A) ~ year) |> fitted() |> exp()) |>
  transmute(date,
            `Government Purchases (% of GDP)` = government_gdp,
            `Government Purchases (% of Log-Linear GDP Trend)` = government_gdp * gdp_real_A / gdp_real_A_LLtrend,
            `Government Purchases (% of HP GDP Trend)` = government_gdp * gdp_real_A / gdp_real_A_hp10000) |>
  gather(variable, value, -date) |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable, linetype = variable)) + theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.15)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%Y"),
               limits = as.Date(paste0(c(1947, 2019), "-01-01"))) +
  scale_y_continuous(breaks = seq(0.12, 0.29, 0.01),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(0.12, 0.29)) + 
  xlab("") + ylab("Government Purchases (% of GDP)")

(ref:g-purchases)

Residential investment

Code 
T40100_A |>
  year_to_date() |>
  filter(LineNumber %in% c(1, 13)) |>
  group_by(date) |>
  filter(n() == 2) |>
  summarise(government_gdp = DataValue[LineNumber == 13]/DataValue[LineNumber == 1]) |>
  select(date, government_gdp) |>
  inner_join(gdp_real_A, by = "date") |>
  mutate(year = year(date),
         gdp_real_A_hp10000 = log(gdp_real_A) |> hpfilter(freq = 10000) |> pluck("trend") |> exp(),
         gdp_real_A_LLtrend = lm(log(gdp_real_A) ~ year) |> fitted() |> exp()) |>
  transmute(date,
            `Residential Investment (% of GDP)` = government_gdp,
            `Residential Investment (% of Log-Linear GDP Trend)` = government_gdp * gdp_real_A / gdp_real_A_LLtrend,
            `Residential Investment (% of HP GDP Trend)` = government_gdp * gdp_real_A / gdp_real_A_hp10000) |>
  gather(variable, value, -date) |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable, linetype = variable)) + theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.15)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%Y"),
               limits = as.Date(paste0(c(1947, 2019), "-01-01"))) +
  scale_y_continuous(breaks = seq(0, 0.29, 0.01),
                     labels = scales::percent_format(accuracy = 1)) + 
  xlab("") + ylab("Government Purchases (% of GDP)")

Net Exports

HP Filter

Code 
T40100_A |>
  year_to_date() |>
  filter(LineNumber %in% c(1, 15)) |>
  group_by(date) |>
  filter(n() == 2) |>
  summarise(net_exports_gdp = DataValue[LineNumber == 15]/DataValue[LineNumber == 1]) |>
  select(date, net_exports_gdp) |>
  inner_join(gdp_real_A, by = "date") |>
  mutate(year = year(date),
         gdp_real_A_hp10000 = log(gdp_real_A) |> hpfilter(freq = 10000) |> pluck("trend") |> exp(),
         gdp_real_A_LLtrend = lm(log(gdp_real_A) ~ year) |> fitted() |> exp()) |>
  transmute(date,
            `Net Exports (% of GDP)` = net_exports_gdp,
            `Net Exports (% of Log-Linear GDP Trend)` = net_exports_gdp * gdp_real_A / gdp_real_A_LLtrend,
            `Net Exports (% of HP GDP Trend)` = net_exports_gdp * gdp_real_A / gdp_real_A_hp10000) |>
  gather(variable, value, -date) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Net Exports (% of GDP)") +
  geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black") +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.75, 0.85)) +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + 
  scale_y_continuous(breaks = seq(-0.06, 0.05, 0.01),
                     labels = scales::percent_format(accuracy = 1))

No HP Filter

Code 
T40100_A |>
  year_to_date() |>
  filter(LineNumber %in% c(1, 15)) |>
  group_by(date) |>
  filter(n() == 2) |>
  summarise(net_exports_gdp = DataValue[LineNumber == 15]/DataValue[LineNumber == 1]) |>
  select(date, net_exports_gdp) |>
  inner_join(gdp_real_A, by = "date") |>
  mutate(year = year(date),
         gdp_real_A_LLtrend = exp(fitted(lm(log(gdp_real_A) ~ year)))) |>
  transmute(date,
            `Net Exports (% of GDP)` = net_exports_gdp,
            `Net Exports (% of Log-Linear GDP Trend)` = net_exports_gdp * gdp_real_A / gdp_real_A_LLtrend) |>
  gather(variable, value, -date) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Net Exports (% of GDP)") +
  geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black") +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.75, 0.85)) +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + 
  scale_y_continuous(breaks = seq(-0.06, 0.05, 0.01),
                     labels = scales::percent_format(accuracy = 1))