source | dataset | .html | .RData |
---|---|---|---|
bea | T10206_A | NA | 2023-12-06 |
bea | T10206_Q | NA | 2023-12-06 |
source | dataset | .html | .RData |
---|---|---|---|
fred | gdp | 2024-02-03 | 2024-02-11 |
fred | unr | 2024-02-03 | 2024-02-03 |
oecd | QNA | 2024-01-28 | 2024-02-03 |
oecd | SNA_TABLE1 | 2024-01-28 | 2024-02-03 |
file | LAST_DOWNLOAD |
---|---|
T10206_Q | 2023-12-06 |
T10206_A | 2023-12-06 |
LAST_COMPILE |
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2024-02-11 |
date | Nobs |
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2023-09-30 | 22 |
fit_Q <- T10206_Q %>%
quarter_to_date %>%
filter(LineNumber == 1) %>%
select(date, `Real GDP` = DataValue) %>%
mutate(`Real GDP` = `Real GDP` / 1000000,
datenum = year(date)+(month(date)-1)/12) %>%
filter(datenum <= 1971) %>%
lm(log(`Real GDP`) ~ datenum, data = .) %>%
pluck("coefficients")
T10206_Q %>%
quarter_to_date %>%
filter(LineNumber == 1) %>%
select(date, `Real GDP` = DataValue) %>%
mutate(`Real GDP` = `Real GDP` / 1000000,
datenum = year(date)+(month(date)-1)/12) %>%
mutate(`Trend to Today` = lm(log(`Real GDP`) ~ datenum) %>% fitted %>% exp,
`Trend to 1971, extrapolated` = ifelse(datenum >= 1971, (fit_Q[1] + fit_Q[2] * datenum) %>% exp, NA),
`Trend to 1971` = ifelse(datenum <= 1971, (fit_Q[1] + fit_Q[2] * datenum) %>% exp, NA)) %>%
group_by(date) %>%
select(-datenum) %>%
gather(variable, value, -date) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
theme_minimal() +
scale_color_manual(values = c(viridis(4)[3], viridis(4)[1], viridis(4)[1], viridis(4)[2])) +
scale_linetype_manual(values = c("solid", "solid", "dashed", "solid")) +
theme(legend.title = element_blank(),
legend.position = c(0.8, 0.2)) +
scale_x_date(breaks = nber_recessions$Peak,
labels = date_format("%y")) +
scale_y_log10(breaks = 20*2^seq(-7, 1, 1),
labels = scales::dollar_format(accuracy = 0.1, suffix = "Tn")) +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1947-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
xlab("") + ylab("U.S. Real GDP, $2012")
fit_Q_1980 <- T10206_Q %>%
quarter_to_date %>%
filter(date >= as.Date("1980-01-01")) %>%
filter(LineNumber == 1) %>%
select(date, `Real GDP` = DataValue) %>%
mutate(`Real GDP` = `Real GDP` / 1000000,
datenum = year(date)+(month(date)-1)/12) %>%
filter(datenum <= 2007) %>%
lm(log(`Real GDP`) ~ datenum, data = .) %>%
pluck("coefficients")
T10206_Q %>%
quarter_to_date %>%
filter(date >= as.Date("1980-01-01")) %>%
filter(LineNumber == 1) %>%
select(date, `Real GDP` = DataValue) %>%
mutate(`Real GDP` = `Real GDP` / 1000000,
datenum = year(date)+(month(date)-1)/12) %>%
mutate(`Trend to Today` = lm(log(`Real GDP`) ~ datenum) %>% fitted %>% exp,
`Trend to 2008, extrapolated` = ifelse(datenum >= 1971, (fit_Q_1980[1] + fit_Q_1980[2] * datenum) %>% exp, NA),
`Trend to 2008` = ifelse(datenum <= 1971, (fit_Q_1980[1] + fit_Q_1980[2] * datenum) %>% exp, NA)) %>%
group_by(date) %>%
select(-datenum) %>%
gather(variable, value, -date) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
theme_minimal() +
scale_color_manual(values = c(viridis(4)[3], viridis(4)[1], viridis(4)[1], viridis(4)[2])) +
scale_linetype_manual(values = c("solid", "solid", "dashed", "solid")) +
theme(legend.title = element_blank(),
legend.position = c(0.8, 0.2)) +
scale_x_date(breaks = nber_recessions$Peak,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(1, 100, 1),
labels = scales::dollar_format(accuracy = 1, suffix = "Tn")) +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1980-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
xlab("") + ylab("U.S. Real GDP, $2012")
fit <- T10206_A %>%
year_to_date %>%
filter(LineNumber == 1) %>%
select(date, `Real GDP` = DataValue) %>%
mutate(`Real GDP` = `Real GDP` / 1000000,
year = year(date)) %>%
filter(year <= 1971) %>%
lm(log(`Real GDP`) ~ year, data = .) %>%
pluck("coefficients")
T10206_A %>%
year_to_date %>%
filter(LineNumber == 1) %>%
select(date, `Real GDP` = DataValue) %>%
mutate(`Real GDP` = `Real GDP` / 1000000,
year = year(date)) %>%
mutate(`Trend to Today` = lm(log(`Real GDP`) ~ year) %>% fitted %>% exp,
`Trend to 1971, extrapolated` = ifelse(year >= 1971, (fit[1] + fit[2] * year) %>% exp, NA),
`Trend to 1971` = ifelse(year <= 1971, (fit[1] + fit[2] * year) %>% exp, NA)) %>%
group_by(date) %>%
select(-year) %>%
gather(variable, value, -date) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
theme_minimal() +
scale_color_manual(values = c(viridis(4)[3], viridis(4)[1], viridis(4)[1], viridis(4)[2])) +
scale_linetype_manual(values = c("solid", "solid", "dashed", "solid")) +
theme(legend.title = element_blank(),
legend.position = c(0.8, 0.2)) +
scale_x_date(breaks = nber_recessions$Peak,
labels = date_format("%y")) +
scale_y_log10(breaks = 20*2^seq(-7, 1, 1),
labels = scales::dollar_format(accuracy = 0.1, suffix = "Tn")) +
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) +
xlab("") + ylab("U.S. Real GDP, $2012")
T10206_A %>%
year_to_date %>%
filter(LineNumber == 1) %>%
select(date, `Real GDP` = DataValue) %>%
mutate(`Real GDP` = `Real GDP` / 1000000,
year = year(date)) %>%
mutate(`Trend to 2019 (g = 3.5%)` = lm(log(`Real GDP`) ~ year) %>% fitted %>% exp,
`Trend to 1971, extrapolated` = ifelse(year >= 1971, (fit[1] + fit[2] * year) %>% exp, NA),
`Trend to 1971 (g = 4.1%)` = ifelse(year <= 1971, (fit[1] + fit[2] * year) %>% exp, NA)) %>%
group_by(date) %>%
select(-year) %>%
gather(variable, value, -date) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
theme_minimal() +
scale_color_manual(values = c(viridis(4)[3], viridis(4)[1], viridis(4)[1], viridis(4)[2])) +
scale_linetype_manual(values = c("solid", "solid", "dashed", "solid")) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.8)) +
scale_x_date(breaks = nber_recessions$Peak,
labels = date_format("%y")) +
scale_y_continuous(breaks = 20*2^seq(-4, 1, 1),
labels = scales::dollar_format(accuracy = 0.1, suffix = "Tn")) +
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) +
xlab("") + ylab("U.S. Real GDP, $2012")
T10206_A %>%
year_to_date %>%
filter(LineNumber == 1) %>%
select(date, `Real GDP` = DataValue) %>%
mutate(`Real GDP` = `Real GDP` / 1000000,
year = year(date)) %>%
mutate(`Cycle, Detrending to 2019 (g = 3.5%)` = lm(log(`Real GDP`) ~ year) %>% residuals %>% exp,
`Cycle, Detrending to 1971, extrapolated` = ifelse(year >= 1971, exp(log(`Real GDP`) - fit[1] - fit[2] * year), NA),
`Cycle, Detrending to 1971 (g = 4.1%)` = ifelse(year <= 1971, exp(log(`Real GDP`) - fit[1] - fit[2] * year), NA)) %>%
group_by(date) %>%
select(-year, -`Real GDP`) %>%
gather(variable, value, -date) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = variable, linetype = variable)) +
theme_minimal() +
scale_color_manual(values = c(viridis(4)[1],viridis(4)[1], viridis(4)[2])) +
scale_linetype_manual(values = c("solid", "dashed", "solid")) +
theme(legend.title = element_blank(),
legend.position = c(0.7, 0.8)) +
scale_x_date(breaks = nber_recessions$Peak,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 2.25, 0.10),
labels = scales::percent_format(accuracy = 1),
limits = c(0.4, 1.55)) +
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) +
xlab("") + ylab("U.S. Real GDP, $2012")
T10206_A %>%
year_to_date %>%
mutate(year = year(date)) %>%
filter(year %in% c(1938, 1958, 1978, 1998, 2018)) %>%
group_by(year) %>%
mutate(value = round(100*DataValue/DataValue[1], 1)) %>%
ungroup %>%
select(2, 3, 6, 7) %>%
spread(year, value) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
T10206_A %>%
year_to_date %>%
mutate(year = year(date)) %>%
filter(year %in% c(1938, 1958, 1978, 1998, 2018)) %>%
group_by(year) %>%
mutate(value = round(DataValue/1000)) %>%
ungroup %>%
select(2, 3, 6, 7) %>%
spread(year, value) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}