DBnomics’ API

Data - rdb

~/data/rdb/

Info

source	dataset	.html	.RData
rdb	api	NA	NA

List of APIs

source	dataset	.html	.RData
bdf	api	2024-05-24	NA
bea	api	2024-02-11	NA
bis	api	2024-04-19	NA
bls	api	2024-02-11	NA
ecb	api	2024-05-23	NA
eurostat	api	2024-06-04	NA
imf	api	2024-02-11	NA
insee	api	2024-05-30	NA
oecd	api	2024-05-07	2024-04-16
rdb	api	NA	NA
wdi	api	2024-04-14	NA

All

• Use the JSON RESTful Web Service. html

• List of providers. html

LAST_COMPILE

LAST_COMPILE
2024-06-07

Nominal GDP

rdb(ids = c("OECD/QNA/FRA.B1_GE.CARSA.Q",
            "OECD/QNA/USA.B1_GE.CARSA.Q",
            "OECD/QNA/ITA.B1_GE.CARSA.Q",
            "OECD/QNA/GBR.B1_GE.CARSA.Q",
            "OECD/QNA/ESP.B1_GE.CARSA.Q",
            "OECD/QNA/DEU.B1_GE.CARSA.Q")) %>%
  filter(period >= as.Date("2019-10-01")) %>%
  select(Country, period, value) %>%
  group_by(Country) %>%
  mutate(value = 100*value/value[1]) %>%
  ggplot + geom_line(aes(x = period, y = value, color = Country)) +
  theme_minimal() + xlab("") + ylab("Real GDP") +
  theme(legend.position = c(0.85, 0.3),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_date(breaks = "3 months",
               labels = date_format("%m-%Y")) +
  scale_y_continuous(breaks = seq(80, 200, 2))

Real GDP

rdb(ids = c("OECD/QNA/FRA.B1_GE.VOBARSA.Q",
            "OECD/QNA/USA.B1_GE.VOBARSA.Q",
            "OECD/QNA/ITA.B1_GE.VOBARSA.Q",
            "OECD/QNA/GBR.B1_GE.VOBARSA.Q",
            "OECD/QNA/ESP.B1_GE.VOBARSA.Q",
            "OECD/QNA/DEU.B1_GE.VOBARSA.Q")) %>%
  filter(period >= as.Date("2019-10-01")) %>%
  select(Country, period, value) %>%
  group_by(Country) %>%
  mutate(value = 100*value/value[1]) %>%
  ggplot + geom_line(aes(x = period, y = value, color = Country)) +
  theme_minimal() + xlab("") + ylab("Real GDP") +
  theme(legend.position = c(0.85, 0.3),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_date(breaks = "3 months",
               labels = date_format("%m-%Y")) +
  scale_y_continuous(breaks = seq(80, 120, 2))

U.S. Real GDP Cycle

rdb(ids = "BEA/NIPA-T10106/A191RX-A") %>%
  select(date = period, `Real GDP` = value) %>%
  mutate(year = lubridate::year(date)) %>%
  mutate(`HP (1000) Cycle` = exp(hpfilter(log(`Real GDP`), freq = 1000, 
                                          type = "lambda", drift = FALSE)$cycle),
         `HP (10000) Cycle` = exp(hpfilter(log(`Real GDP`), freq = 10000, 
                                           type = "lambda", drift = FALSE)$cycle),
         `Log-Linear Cycle` = exp(residuals(lm(log(`Real GDP`) ~ year, 
                                               na.action = na.exclude)))) %>%
  group_by(date) %>%
  select(-year, - `Real GDP`) %>%
  gather(variable, value, -date) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, linetype = variable)) + 
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = c(0.8, 0.8)) +
  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")) +
  scale_y_continuous(breaks = seq(0.60, 1.40, 0.05),
                     labels = scales::percent_format(accuracy = 1)) + 
  xlab("") + ylab("GDP Cycle (% of Trend)")

U.S. Durable Goods

rdb(ids = c("BEA/NIPA-T10106/A191RX-A",
            "BEA/NIPA-T10105/DDURRC-A",
            "BEA/NIPA-T10105/A191RC-A")) %>%
  select(series_code, date = period, value) %>%
  spread(series_code, value) %>%
  rename(GDP = `A191RC-A`,
         `Durable Goods` = `DDURRC-A`,
         `Real GDP` = `A191RX-A`) %>%
  mutate(year = lubridate::year(date),
         `Real GDP HP (10000) Trend` = exp(hpfilter(log(`Real GDP`), freq = 10000, 
                                                    type = "lambda", drift = FALSE)$trend),
         `Real GDP Log-Linear Trend` = exp(fitted(lm(log(`Real GDP`) ~ year, 
                                                     na.action = na.exclude))),
         `Durable Goods (% of GDP)` = `Durable Goods` / GDP,
         `Durable Goods (% of Log-Linear GDP Trend)` = 
           (`Durable Goods` / GDP)*(`Real GDP` / `Real GDP Log-Linear Trend`),
         `Durable Goods (% of HP GDP Trend)` = 
           (`Durable Goods` / GDP)*(`Real GDP` / `Real GDP HP (10000) Trend`)) %>%
  select(date, contains("Durable Goods (%")) %>%
  gather(variable, value, -date) %>%
  ggplot(.) +
  geom_line(aes(x = date, y = value, linetype = variable)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.85)) +
  geom_rect(data = nber_recessions %>%
              filter(Peak >= as.Date("1928-01-01")),
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%y")) +
  scale_y_continuous(breaks = 0.01*seq(2, 16, 1),
                     limits = 0.01*c(2, 16),
                     labels = scales::percent_format(accuracy = 1)) + 
  xlab("") + ylab("Durable Goods (% of GDP)")