Table 1.1.5. Gross Domestic Product (A) (Q)

Data - BEA

Author

François Geerolf

Info

source dataset .html .RData

bea

T10105

 2024-05-19 2023-12-17

bea

T10106

 2024-05-20 2024-05-19

Data on US macro

source dataset .html .RData

fred

gdp

 2024-08-29 2024-09-18

fred

unr

 2024-09-18 2024-09-18

oecd

QNA

 2024-06-06 2024-06-30

oecd

SNA_TABLE1

 2024-09-15 2024-06-30

LAST_COMPILE

LAST_COMPILE
2024-09-30

Last

date Nobs
2024-06-30 26

Layout

  • NIPA Website. html

LineNumber, LineDescription

Code 
T10105_A %>%
  group_by(LineNumber, LineDescription) %>%
  summarise(Nobs = n())
# # A tibble: 26 × 3
# # Groups:   LineNumber [26]
#    LineNumber LineDescription                    Nobs
#         <dbl> <chr>                             <int>
#  1          1 Gross domestic product               95
#  2          2 Personal consumption expenditures    95
#  3          3 Goods                                95
#  4          4 Durable goods                        95
#  5          5 Nondurable goods                     95
#  6          6 Services                             95
#  7          7 Gross private domestic investment    95
#  8          8 Fixed investment                     95
#  9          9 Nonresidential                       95
# 10         10 Structures                           95
# # ℹ 16 more rows

Imports and Exports

Graph, Annual

Code 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 16, 19)) %>%
  group_by(date) %>%
  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, 2025, 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 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 17, 18, 20, 21)) %>%
  group_by(date) %>%
  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, 2025, 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 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 17, 18, 20, 21)) %>%
  group_by(date) %>%
  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, 2025, 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 
T10105_Q %>%
  quarter_to_date %>%
  filter(LineNumber %in% c(1, 15)) %>%
  group_by(date) %>%
  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, 2025, 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 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 15)) %>%
  group_by(date) %>%
  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, 2025, 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"))
T10105_Q %>%
  quarter_to_date %>%
  filter(LineNumber %in% c(1, 15)) %>%
  group_by(date) %>%
  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 
T10105_Q %>%
  quarter_to_date %>%
  filter(SeriesCode %in% c("A191RC", "A019RC"),
         date >= as.Date("2016-12-01")) %>%
  select(SeriesCode, date, DataValue) %>%
  spread(SeriesCode, DataValue) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Data - Plots

Code 
T10105_Q %>%
  quarter_to_date %>%
  filter(SeriesCode %in% c("A191RC", "A019RC"),
         date >= as.Date("2017-01-01")) %>%
  select(SeriesCode, date, DataValue) %>%
  spread(SeriesCode, DataValue) %>%
  ggplot() + geom_line(aes(x = date, y = A019RC/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 
T10105_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
Gross domestic product 100 % 100 % 100 % 100 % 100 % 100 %
Personal consumption expenditures 74 % 65.4 % 59.3 % 63.4 % 68.3 % 67 %
Goods 41.9 % 39.7 % 29.9 % 25.2 % 22 % 21 %
Durable goods 9.4 % 9.8 % 8.9 % 8.8 % 7 % 7.1 %
Nondurable goods 32.5 % 29.9 % 21 % 16.5 % 15 % 14 %
Services 32.1 % 25.8 % 29.4 % 38.2 % 46.4 % 46 %
Gross private domestic investment 16.4 % 14.3 % 17.1 % 17.7 % 13.3 % 18.1 %
Fixed investment 14.9 % 15.3 % 16.2 % 17.2 % 14.4 % 17.7 %
Nonresidential 11.1 % 9.9 % 11.8 % 12.7 % 11.7 % 13.9 %
Structures 5.3 % 3.4 % 3.7 % 3.4 % 3.1 % 3.1 %
Equipment 5.3 % 5.8 % 6.4 % 6.6 % 4.6 % 5.8 %
Intellectual property products 0.6 % 0.7 % 1.7 % 2.7 % 3.9 % 5 %
Residential 3.9 % 5.4 % 4.4 % 4.5 % 2.7 % 3.8 %
Change in private inventories 1.5 % -1 % 0.9 % 0.5 % -1 % 0.3 %
Net exports of goods and services 0.4 % 1.9 % 0.1 % -1.5 % -2.9 % -2.7 %
Exports 5.7 % 5.3 % 5.1 % 8.9 % 10.9 % 11.8 %
Goods 5.1 % 4.5 % 3.8 % 6.6 % 7.3 % 7.6 %
Services 0.6 % 0.8 % 1.3 % 2.3 % 3.6 % 4.2 %
Imports 5.3 % 3.4 % 5 % 10.5 % 13.8 % 14.5 %
Goods 4.3 % 2.5 % 3.6 % 8.6 % 11 % 11.7 %
Services 1 % 0.9 % 1.3 % 1.9 % 2.9 % 2.8 %
Government consumption expenditures and gross investment 9.2 % 18.3 % 23.5 % 20.4 % 21.2 % 17.6 %
Federal 1.8 % 11.2 % 12.9 % 9.5 % 8.4 % 6.6 %
National defense 1 % 8.2 % 10 % 6.9 % 5.4 % 3.9 %
Nondefense 0.8 % 3 % 2.9 % 2.5 % 3 % 2.6 %
State and local 7.4 % 7.1 % 10.6 % 11 % 12.8 % 11 %

Percent - pdf

Code 
include_graphics2("https://fgeerolf.com/bib/bea/T10105_ex.png")

Billions

Code 
T10105_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
Gross domestic product $ 105 Bn $ 272 Bn $ 1018 Bn $ 5642 Bn $ 14478 Bn $ 21540 Bn
Personal consumption expenditures $ 77 Bn $ 178 Bn $ 604 Bn $ 3577 Bn $ 9891 Bn $ 14438 Bn
Goods $ 44 Bn $ 108 Bn $ 305 Bn $ 1424 Bn $ 3180 Bn $ 4533 Bn
Durable goods $ 10 Bn $ 27 Bn $ 90 Bn $ 494 Bn $ 1012 Bn $ 1524 Bn
Nondurable goods $ 34 Bn $ 81 Bn $ 214 Bn $ 929 Bn $ 2168 Bn $ 3009 Bn
Services $ 34 Bn $ 70 Bn $ 299 Bn $ 2153 Bn $ 6711 Bn $ 9905 Bn
Gross private domestic investment $ 17 Bn $ 39 Bn $ 174 Bn $ 1000 Bn $ 1930 Bn $ 3894 Bn
Fixed investment $ 16 Bn $ 42 Bn $ 164 Bn $ 972 Bn $ 2080 Bn $ 3821 Bn
Nonresidential $ 12 Bn $ 27 Bn $ 120 Bn $ 716 Bn $ 1690 Bn $ 2994 Bn
Structures $ 5 Bn $ 9 Bn $ 38 Bn $ 194 Bn $ 456 Bn $ 678 Bn
Equipment $ 5 Bn $ 16 Bn $ 65 Bn $ 372 Bn $ 670 Bn $ 1241 Bn
Intellectual property products $ 1 Bn $ 2 Bn $ 17 Bn $ 150 Bn $ 564 Bn $ 1075 Bn
Residential $ 4 Bn $ 15 Bn $ 44 Bn $ 256 Bn $ 390 Bn $ 827 Bn
Change in private inventories $ 2 Bn $ -3 Bn $ 9 Bn $ 28 Bn $ -151 Bn $ 73 Bn
Net exports of goods and services $ 0 Bn $ 5 Bn $ 1 Bn $ -87 Bn $ -419 Bn $ -577 Bn
Exports $ 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
Services $ 1 Bn $ 2 Bn $ 13 Bn $ 130 Bn $ 525 Bn $ 895 Bn
Imports $ 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
Services $ 1 Bn $ 2 Bn $ 14 Bn $ 106 Bn $ 414 Bn $ 600 Bn
Government consumption expenditures and gross investment $ 10 Bn $ 50 Bn $ 239 Bn $ 1152 Bn $ 3076 Bn $ 3786 Bn
Federal $ 2 Bn $ 30 Bn $ 131 Bn $ 534 Bn $ 1221 Bn $ 1420 Bn
National defense $ 1 Bn $ 22 Bn $ 102 Bn $ 391 Bn $ 788 Bn $ 850 Bn
Nondefense $ 1 Bn $ 8 Bn $ 29 Bn $ 143 Bn $ 433 Bn $ 570 Bn
State and local $ 8 Bn $ 19 Bn $ 108 Bn $ 618 Bn $ 1856 Bn $ 2366 Bn

Econ 102

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

Linear

Code 
plot_linear <- T10105_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 
T10105_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 
T10105_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 
T10105_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 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 13)) %>%
  group_by(date) %>%
  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 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 15)) %>%
  group_by(date) %>%
  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, 2025, 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 
T10105_A %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 15)) %>%
  group_by(date) %>%
  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, 2025, 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))