| source | dataset | .html | .RData |
|---|---|---|---|
| bis | CPI | 2024-07-01 | 2022-01-20 |
| ecb | CES | 2024-11-17 | 2024-11-17 |
| eurostat | nama_10_co3_p3 | 2024-11-08 | 2024-10-09 |
| eurostat | prc_hicp_cow | 2024-11-05 | 2024-10-08 |
| eurostat | prc_hicp_ctrb | 2024-11-05 | 2024-10-08 |
| eurostat | prc_hicp_inw | 2024-11-05 | 2024-11-21 |
| eurostat | prc_hicp_manr | 2024-11-17 | 2024-11-17 |
| eurostat | prc_hicp_midx | 2024-11-01 | 2024-11-21 |
| eurostat | prc_hicp_mmor | 2024-11-05 | 2024-11-21 |
| eurostat | prc_ppp_ind | 2024-11-05 | 2024-10-08 |
| eurostat | sts_inpp_m | 2024-06-24 | 2024-11-17 |
| eurostat | sts_inppd_m | 2024-11-17 | 2024-11-17 |
| eurostat | sts_inppnd_m | 2024-06-24 | 2024-11-17 |
| fred | cpi | 2024-11-21 | 2024-11-21 |
| fred | inflation | 2024-11-21 | 2024-11-21 |
| imf | CPI | 2024-06-20 | 2020-03-13 |
| oecd | MEI_PRICES_PPI | 2024-09-15 | 2024-04-15 |
| oecd | PPP2017 | 2024-04-16 | 2023-07-25 |
| oecd | PRICES_CPI | 2024-04-16 | 2024-04-15 |
| wdi | FP.CPI.TOTL.ZG | 2023-01-15 | 2024-09-18 |
| wdi | NY.GDP.DEFL.KD.ZG | 2024-09-18 | 2024-09-18 |
Consumer Price Index
Data - Fred
Info
Data on inflation
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-11-22 |
Last
| date | Nobs |
|---|---|
| 2024-10-01 | 56 |
variable
All
Code
cpi %>%
left_join(variable, by = "variable") %>%
group_by(variable, Variable) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()CBSA
Code
cpi %>%
left_join(variable, by = "variable") %>%
filter(grepl("CBSA", Variable)) %>%
group_by(variable, Variable) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Different Measures of inflation
Trimmed Inflation Rate,
Implicit Price Deflator - Imports of goods
All
Code
cpi %>%
filter(variable == "A255RD3Q086SBEA") %>%
ggplot() + ylab("Imports of goods (implicit price deflator)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value)) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
1980-
Code
cpi %>%
filter(variable == "A255RD3Q086SBEA",
date >= as.Date("1980-01-01")) %>%
ggplot() + ylab("Imports of goods (implicit price deflator)") + xlab("") +
theme_minimal() + geom_line(aes(x = date, y = value)) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
1990-
Code
cpi %>%
filter(variable == "A255RD3Q086SBEA",
date >= as.Date("1990-01-01")) %>%
ggplot() + ylab("Imports of goods (implicit price deflator)") + xlab("") +
theme_minimal() + geom_line(aes(x = date, y = value)) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2000-
Code
cpi %>%
filter(variable == "A255RD3Q086SBEA",
date >= as.Date("2000-01-01")) %>%
ggplot() + ylab("Imports of goods (implicit price deflator)") + xlab("") +
theme_minimal() + geom_line(aes(x = date, y = value)) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2010-
Code
cpi %>%
filter(variable == "A255RD3Q086SBEA",
date >= as.Date("2010-01-01")) %>%
ggplot() + ylab("Imports of goods (implicit price deflator)") + xlab("") +
theme_minimal() + geom_line(aes(x = date, y = value)) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
CPI
Log
Code
cpi %>%
filter(variable %in% c("CPIAUCSL")) %>%
ggplot + geom_line(aes(x = date, y = value)) +
scale_y_log10(breaks = c(30, 50, 80, 100, 200, 300)) +
theme(legend.position = c(0.15, 0.85),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_rect(data = nber_recessions %>%
filter(Peak >= as.Date("1946-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
theme_minimal() + labs(x = "", y = "")
U.S. CPI Components
1997 - 2019
Linear
Code
date0 <- as.Date("2019-03-01")
date1 <- as.Date("2020-12-01")
cpi %>%
filter(variable %in% c("CUUR0000SEEB", "CPIAUCSL", "CPIMEDSL", "CUSR0000SERA02", "CUSR0000SEEA",
"CUUR0000SEMD", "CUSR0000SERE01", "CPIAPPSL", "CUUR0000SETA01", "CUUR0000SEHA"),
date >= as.Date("1997-01-01")) %>%
arrange(date) %>%
group_by(variable) %>%
mutate(value = 100 * value / value[1]) %>%
ungroup %>%
spread(variable, value) %>%
ggplot(.) + scale_color_viridis(discrete = TRUE) +
geom_line(color = viridis(10)[1], aes(x = date, y = CUUR0000SEEB)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[1], aes(x = date1, y = CUUR0000SEEB, label = "Tuition, childcare")) +
geom_line(color = viridis(10)[2], linetype = 2, aes(x = date, y = CPIAUCSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[2], aes(x = date1, y = CPIAUCSL, label = "General Price Index")) +
geom_line(color = viridis(10)[3], linetype = 3, aes(x = date, y = CPIMEDSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[3], aes(x = date1, y = CPIMEDSL, label = "Medical Care")) +
geom_line(color = viridis(10)[4], linetype = 4, aes(x = date, y = CUSR0000SEEA)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[4], aes(x = date1, y = CUSR0000SEEA, label = "Educational Books")) +
geom_line(color = viridis(10)[5], linetype = 5, aes(x = date, y = CUUR0000SEMD)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[5], aes(x = date1, y = CUUR0000SEMD, label = "Hospital Services")) +
geom_line(color = viridis(10)[6], linetype = 6, aes(x = date, y = CUSR0000SERE01)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[6], aes(x = date1, y = CUSR0000SERE01, label = "Toys")) +
geom_line(color = viridis(10)[7], linetype = 7, aes(x = date, y = CPIAPPSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[7], aes(x = date1, y = CPIAPPSL, label = "Apparel")) +
geom_line(color = viridis(10)[8], linetype = 8, aes(x = date, y = CUUR0000SETA01)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[8], aes(x = date1, y = CUUR0000SETA01, label = "New vehicles")) +
geom_line(color = viridis(10)[9], linetype = 9, aes(x = date, y = CUUR0000SEHA)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[9], aes(x = date1, y = CUUR0000SEHA, label = "Rents")) +
scale_x_date(breaks = seq(1940, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y"),
limits = c(1997, 2025) %>% paste0("-01-01") %>% as.Date) +
scale_y_continuous(breaks = seq(0, 600, 20)) +
geom_rect(data = nber_recessions,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
theme(legend.position = c(0.15, 0.85),
legend.title = element_blank()) +
theme_minimal() + labs(x = "", y = "")
Log
Code
date0 <- as.Date("2019-03-01")
date1 <- as.Date("2020-12-01")
cpi %>%
filter(variable %in% c("CUUR0000SEEB", "CPIAUCSL", "CPIMEDSL", "CUSR0000SERA02", "CUSR0000SEEA",
"CUUR0000SEMD", "CUSR0000SERE01", "CPIAPPSL", "CUUR0000SETA01", "CUUR0000SEHA"),
date >= as.Date("1997-01-01")) %>%
arrange(date) %>%
group_by(variable) %>%
mutate(value = 100 * value / value[1]) %>%
ungroup %>%
spread(variable, value) %>%
ggplot(.) + scale_color_viridis(discrete = TRUE) +
geom_line(color = viridis(10)[1], aes(x = date, y = CUUR0000SEEB)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[1], aes(x = date1, y = CUUR0000SEEB, label = "Tuition, childcare")) +
geom_line(color = viridis(10)[2], linetype = 2, aes(x = date, y = CPIAUCSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[2], aes(x = date1, y = CPIAUCSL, label = "General Price Index")) +
geom_line(color = viridis(10)[3], linetype = 3, aes(x = date, y = CPIMEDSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[3], aes(x = date1, y = CPIMEDSL, label = "Medical Care")) +
geom_line(color = viridis(10)[4], linetype = 4, aes(x = date, y = CUSR0000SEEA)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[4], aes(x = date1, y = CUSR0000SEEA, label = "Educational Books")) +
geom_line(color = viridis(10)[5], linetype = 5, aes(x = date, y = CUUR0000SEMD)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[5], aes(x = date1, y = CUUR0000SEMD, label = "Hospital Services")) +
geom_line(color = viridis(10)[6], linetype = 6, aes(x = date, y = CUSR0000SERE01)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[6], aes(x = date1, y = CUSR0000SERE01, label = "Toys")) +
geom_line(color = viridis(10)[7], linetype = 7, aes(x = date, y = CPIAPPSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[7], aes(x = date1, y = CPIAPPSL, label = "Apparel")) +
geom_line(color = viridis(10)[8], linetype = 8, aes(x = date, y = CUUR0000SETA01)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[8], aes(x = date1, y = CUUR0000SETA01, label = "New vehicles")) +
geom_line(color = viridis(10)[9], linetype = 9, aes(x = date, y = CUUR0000SEHA)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[9], aes(x = date1, y = CUUR0000SEHA, label = "Rents")) +
scale_x_date(breaks = seq(1940, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y"),
limits = c(1997, 2025) %>% paste0("-01-01") %>% as.Date) +
scale_y_log10(breaks = c(10, 20, 30, 40, 60, 80, 100, 150, 200, 250, 300, 400)) +
geom_rect(data = nber_recessions,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
theme(legend.position = c(0.15, 0.85),
legend.title = element_blank()) +
theme_minimal() + labs(x = "", y = "")
1977 - 2019
Linear
Code
date0 <- as.Date("2019-03-01")
date1 <- as.Date("2020-12-01")
cpi %>%
filter(variable %in% c("CUUR0000SEEB", "CPIAUCSL", "CPIMEDSL", "CUSR0000SERA02", "CUSR0000SEEA",
"CUUR0000SEMD", "CUSR0000SERE01", "CPIAPPSL", "CUUR0000SETA01", "CUUR0000SEHA"),
date >= as.Date("1977-01-01")) %>%
arrange(date) %>%
group_by(variable) %>%
mutate(value = 100 * value / value[1]) %>%
ungroup %>%
spread(variable, value) %>%
ggplot(.) + scale_color_viridis(discrete = TRUE) +
geom_line(color = viridis(10)[1], aes(x = date, y = CUUR0000SEEB)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[1], aes(x = date1, y = CUUR0000SEEB, label = "Tuition, childcare")) +
geom_line(color = viridis(10)[2], linetype = 2, aes(x = date, y = CPIAUCSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[2], aes(x = date1, y = CPIAUCSL, label = "General Price Index")) +
geom_line(color = viridis(10)[3], linetype = 3, aes(x = date, y = CPIMEDSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[3], aes(x = date1, y = CPIMEDSL, label = "Medical Care")) +
geom_line(color = viridis(10)[4], linetype = 4, aes(x = date, y = CUSR0000SEEA)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[4], aes(x = date1, y = CUSR0000SEEA, label = "Educational Books")) +
geom_line(color = viridis(10)[5], linetype = 5, aes(x = date, y = CUUR0000SEMD)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[5], aes(x = date1, y = CUUR0000SEMD, label = "Hospital Services")) +
geom_line(color = viridis(10)[6], linetype = 6, aes(x = date, y = CUSR0000SERE01)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[6], aes(x = date1, y = CUSR0000SERE01, label = "Toys")) +
geom_line(color = viridis(10)[7], linetype = 7, aes(x = date, y = CPIAPPSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[7], aes(x = date1, y = CPIAPPSL, label = "Apparel")) +
geom_line(color = viridis(10)[8], linetype = 8, aes(x = date, y = CUUR0000SETA01)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[8], aes(x = date1, y = CUUR0000SETA01, label = "New vehicles")) +
geom_line(color = viridis(10)[9], linetype = 9, aes(x = date, y = CUUR0000SEHA)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[9], aes(x = date1, y = CUUR0000SEHA, label = "Rents")) +
scale_x_date(breaks = seq(1940, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y"),
limits = c(1977, 2025) %>% paste0("-01-01") %>% as.Date) +
scale_y_continuous(breaks = seq(0, 2000, 100)) +
geom_rect(data = nber_recessions,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
theme(legend.position = c(0.15, 0.85),
legend.title = element_blank()) +
theme_minimal() + labs(x = "", y = "")
Log
Code
date0 <- as.Date("2019-03-01")
date1 <- as.Date("2020-12-01")
cpi %>%
filter(variable %in% c("CUUR0000SEEB", "CPIAUCSL", "CPIMEDSL", "CUSR0000SERA02", "CUSR0000SEEA",
"CUUR0000SEMD", "CUSR0000SERE01", "CPIAPPSL", "CUUR0000SETA01", "CUUR0000SEHA"),
date >= as.Date("1977-01-01")) %>%
arrange(date) %>%
group_by(variable) %>%
mutate(value = 100 * value / value[1]) %>%
ungroup %>%
spread(variable, value) %>%
ggplot(.) + scale_color_viridis(discrete = TRUE) +
geom_line(color = viridis(10)[1], aes(x = date, y = CUUR0000SEEB)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[1], aes(x = date1, y = CUUR0000SEEB, label = "Tuition, childcare")) +
geom_line(color = viridis(10)[2], linetype = 2, aes(x = date, y = CPIAUCSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[2], aes(x = date1, y = CPIAUCSL, label = "General Price Index")) +
geom_line(color = viridis(10)[3], linetype = 3, aes(x = date, y = CPIMEDSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[3], aes(x = date1, y = CPIMEDSL, label = "Medical Care")) +
geom_line(color = viridis(10)[4], linetype = 4, aes(x = date, y = CUSR0000SEEA)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[4], aes(x = date1, y = CUSR0000SEEA, label = "Educational Books")) +
geom_line(color = viridis(10)[5], linetype = 5, aes(x = date, y = CUUR0000SEMD)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[5], aes(x = date1, y = CUUR0000SEMD, label = "Hospital Services")) +
geom_line(color = viridis(10)[6], linetype = 6, aes(x = date, y = CUSR0000SERE01)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[6], aes(x = date1, y = CUSR0000SERE01, label = "Toys")) +
geom_line(color = viridis(10)[7], linetype = 7, aes(x = date, y = CPIAPPSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[7], aes(x = date1, y = CPIAPPSL, label = "Apparel")) +
geom_line(color = viridis(10)[8], linetype = 8, aes(x = date, y = CUUR0000SETA01)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[8], aes(x = date1, y = CUUR0000SETA01, label = "New vehicles")) +
geom_line(color = viridis(10)[9], linetype = 9, aes(x = date, y = CUUR0000SEHA)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[9], aes(x = date1, y = CUUR0000SEHA, label = "Rents")) +
scale_x_date(breaks = seq(1940, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y"),
limits = c(1977, 2025) %>% paste0("-01-01") %>% as.Date) +
scale_y_log10(breaks = c(10, 20, 40, 80, 100, 200, 400, 800, 1000, 2000)) +
geom_rect(data = nber_recessions,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
theme(legend.position = c(0.15, 0.85),
legend.title = element_blank()) +
theme_minimal() + labs(x = "", y = "")
AFGAP Graph
- Association Française des Gestionnaires Actif-Passif - AFGAP. pdf
Code
date0 <- as.Date("2019-03-01")
date1 <- as.Date("2020-12-01")
cpi %>%
filter(variable %in% c("CUUR0000SEEB", "CPIAUCSL", "CPIMEDSL", "CUSR0000SERA02", "CUSR0000SEEA",
"CUUR0000SEMD", "CUSR0000SERE01", "CPIAPPSL", "CUUR0000SETA01", "CUUR0000SEHA"),
date >= as.Date("1977-01-01")) %>%
arrange(date) %>%
group_by(variable) %>%
mutate(value = 100 * value / value[1]) %>%
ungroup %>%
spread(variable, value) %>%
ggplot(.) + scale_color_viridis(discrete = TRUE) +
geom_line(color = viridis(10)[1], aes(x = date, y = CUUR0000SEEB)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[1], aes(x = date1, y = CUUR0000SEEB, label = "Tuition, childcare")) +
geom_line(color = viridis(10)[2], linetype = 2, aes(x = date, y = CPIAUCSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[2], aes(x = date1, y = CPIAUCSL, label = "General Price Index")) +
geom_line(color = viridis(10)[3], linetype = 3, aes(x = date, y = CPIMEDSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[3], aes(x = date1, y = CPIMEDSL, label = "Medical Care")) +
geom_line(color = viridis(10)[4], linetype = 4, aes(x = date, y = CUSR0000SEEA)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[4], aes(x = date1, y = CUSR0000SEEA, label = "Educational Books")) +
geom_line(color = viridis(10)[5], linetype = 5, aes(x = date, y = CUUR0000SEMD)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[5], aes(x = date1, y = CUUR0000SEMD, label = "Hospital Services")) +
geom_line(color = viridis(10)[6], linetype = 6, aes(x = date, y = CUSR0000SERE01)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[6], aes(x = date1, y = CUSR0000SERE01, label = "Toys")) +
geom_line(color = viridis(10)[7], linetype = 7, aes(x = date, y = CPIAPPSL)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[7], aes(x = date1, y = CPIAPPSL, label = "Apparel")) +
geom_line(color = viridis(10)[8], linetype = 8, aes(x = date, y = CUUR0000SETA01)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[8], aes(x = date1, y = CUUR0000SETA01, label = "New vehicles")) +
geom_line(color = viridis(10)[9], linetype = 9, aes(x = date, y = CUUR0000SEHA)) +
geom_text(data = . %>% filter(date == date0), color = viridis(10)[9], aes(x = date1, y = CUUR0000SEHA, label = "Rents")) +
scale_x_date(breaks = seq(1940, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y"),
limits = c(1977, 2025) %>% paste0("-01-01") %>% as.Date) +
scale_y_log10(breaks = c(10, 20, 40, 80, 100, 200, 400, 800, 1000, 2000)) +
geom_rect(data = nber_recessions,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
theme(legend.position = c(0.15, 0.85),
legend.title = element_blank()) +
theme_minimal() + labs(x = "", y = "")
U.S. General Price Index
1913 - 1935
Code
cpi %>%
filter(variable == "CPIAUCNS",
date >= as.Date("1913-01-01"),
date <= as.Date("1935-01-01")) %>%
ggplot(.) +
geom_line(aes(x = date, y = value)) +
ylab("CPI") + xlab("") +
geom_rect(data = nber_recessions,
aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_y_log10(breaks = seq(1, 30, 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1913, 1935, 1), "-01-01")),
labels = date_format("%Y"),
limits = c(as.Date("1913-01-01"), as.Date("1935-01-01"))) +
theme_minimal() +
geom_vline(xintercept = as.Date("1929-10-29"), linetype = "dashed", color = viridis(3)[2])
1913 - 1935
Code
cpi %>%
filter(variable == "CPIAUCNS",
date >= as.Date("1913-01-01"),
date <= as.Date("1945-01-01")) %>%
ggplot(.) +
geom_line(aes(x = date, y = value)) +
ylab("CPI") + xlab("") +
geom_rect(data = nber_recessions,
aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_y_log10(breaks = seq(1, 30, 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1910, 1945, 5), "-01-01")),
labels = date_format("%Y"),
limits = c(as.Date("1913-01-01"), as.Date("1945-01-01"))) +
theme_minimal() +
geom_vline(xintercept = as.Date("1929-10-29"), linetype = "dashed", color = viridis(3)[2])
1922 - 1935
(ref:us-index-cpi) U.S. CPI (1922-1935)
Code
cpi %>%
filter(variable == "CPIAUCNS",
date >= as.Date("1922-01-01"),
date <= as.Date("1935-01-01")) %>%
ggplot(.) +
geom_line(aes(x = date, y = value)) +
ylab("CPI") + xlab("") +
geom_rect(data = nber_recessions,
aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_y_log10(breaks = seq(12, 18, 1),
limits = c(12, 18.5)) +
scale_x_date(breaks = as.Date(paste0(seq(1922, 1935, 1), "-01-01")),
labels = date_format("%Y"),
limits = c(as.Date("1922-01-01"), as.Date("1935-01-01"))) +
theme_minimal() +
geom_vline(xintercept = as.Date("1929-10-29"), linetype = "dashed", color = viridis(3)[2])
1860 - 1939
(ref:us-general-price-1860-1939) U.S. Index of the General Price Level (January 1860 - November 1939)
Code
cpi %>%
filter(variable == "M04051USM324NNBR") %>%
ggplot(.) +
geom_line(aes(x = date, y = value)) +
ylab("CPI") + xlab("") +
geom_rect(data = nber_recessions,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_y_continuous(breaks = seq(50, 200, 10)) +
scale_x_date(breaks = as.Date(paste0(seq(1860, 1939, 10), "-01-01")),
labels = date_format("%Y"),
limits = c(as.Date("1860-01-01"), as.Date("1939-01-01"))) +
theme_minimal() +
geom_vline(xintercept = as.Date("1929-10-29"), linetype = "dashed", color = "red")
1903 - 1925
(ref:us-P-1903-1925-fisher) U.S. Index of the General Price Level (1903-1925)
Code
cpi %>%
filter(variable == "M04051USM324NNBR") %>%
filter(date >= as.Date("1903-01-01"), date <= as.Date("1925-01-01")) %>%
ggplot(.) +
geom_line(aes(x = date, y = value)) +
ylab("CPI") + xlab("") +
geom_rect(data = nber_recessions,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_y_continuous(breaks = seq(50, 200, 10)) +
scale_x_date(breaks = as.Date(paste0(seq(1860, 1939, 5), "-01-01")),
labels = date_format("%Y"),
limits = c(as.Date("1903-01-01"), as.Date("1925-01-01"))) +
theme_minimal() +
geom_vline(xintercept = as.Date("1929-10-29"), linetype = "dashed", color = "red")
Case-Shiller - Fred
1986-
Log
Code
cpi %>%
filter(variable == "CSUSHPINSA") %>%
na.omit %>%
ggplot(.) + ylab("U.S. Case Shiller Price Index") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value)) +
geom_rect(data = nber_recessions %>%
filter(Peak >= as.Date("1987-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_y_log10(breaks = seq(25, 300, 25)) +
scale_x_date(breaks = as.Date(paste0(seq(1900, 2022, 5), "-01-01")),
labels = date_format("%Y"))
Linear
Code
cpi %>%
filter(variable == "CSUSHPINSA") %>%
na.omit %>%
ggplot(.) + ylab("U.S. Case Shiller Price Index") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value)) +
geom_rect(data = nber_recessions %>%
filter(Peak >= as.Date("1987-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_y_continuous(breaks = seq(25, 300, 25)) +
scale_x_date(breaks = as.Date(paste0(seq(1900, 2022, 5), "-01-01")),
labels = date_format("%Y"))
2000-
Code
cpi %>%
filter(variable == "CSUSHPINSA",
date >= as.Date("2000-01-01")) %>%
na.omit %>%
ggplot(.) + ylab("U.S. Case Shiller Price Index") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value)) +
geom_rect(data = nber_recessions %>%
filter(Peak >= as.Date("2000-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_y_log10(breaks = seq(10, 300, 10)) +
scale_x_date(breaks = as.Date(paste0(seq(1900, 2025, 2), "-01-01")),
labels = date_format("%Y"))
2010-
Code
cpi %>%
filter(variable == "CSUSHPINSA",
date >= as.Date("2010-01-01")) %>%
na.omit %>%
ggplot(.) + ylab("U.S. Case Shiller Price Index") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value)) +
scale_y_log10(breaks = seq(10, 300, 10)) +
scale_x_date(breaks = as.Date(paste0(seq(1900, 2025, 1), "-01-01")),
labels = date_format("%Y"))
Rents: Rent of Primary residence
U.S.
Normal scale
Code
cpi %>%
filter(variable %in% c("CUUR0000SEHA", "CPIAUCSL", "CUSR0000SAH1", "CUUR0000SA0L2")) %>%
left_join(variable, by = "variable") %>%
filter(year(date) >= 1947) %>%
ggplot + geom_line(aes(x = date, y = value, color = Variable)) +
ylab("Types of CPI") + xlab("") +
theme_minimal() +
scale_x_date(breaks = seq(1700, 2025, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.90),
legend.title = element_blank(),
legend.key.size = unit(0.9, 'lines')) +
scale_y_continuous(breaks = seq(0, 350, 50))
Log scale
(ref:us-cpi-rents-log) Rents: Rent of Primary residence
Code
cpi %>%
filter(variable %in% c("CUUR0000SEHA", "CPIAUCSL", "CUSR0000SAH1", "CUUR0000SA0L2")) %>%
left_join(variable, by = "variable") %>%
filter(year(date) >= 1947) %>%
ggplot + geom_line(aes(x = date, y = value, color = Variable)) +
ylab("Types of CPI") + xlab("") +
theme_minimal() +
scale_x_date(breaks = seq(1700, 2025, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.90),
legend.title = element_blank(),
legend.key.size = unit(0.9, 'lines')) +
scale_y_log10(breaks = seq(0, 350, 50))
CBSA
Boston, Chicago, New York, Philadelphia
(ref:A101-A102-A103-A207) Boston, Chicago, New York, Philadelphia
Code
cpi %>%
filter(variable %in% c("CUURA101SEHA", "CUURA102SEHA", "CUURA103SEHA", "CUURA207SEHA")) %>%
left_join(variable, by = "variable") %>%
filter(year(date) >= 1978) %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers: Rent of Primary Residence in ", "", Variable)) %>%
ggplot + geom_line(aes(x = date, y = value, color = Variable)) +
ylab("CPI - All Urban Consumers: Rent of Primary Residence") + xlab("") +
theme_minimal() +
scale_x_date(breaks = seq(1700, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.90),
legend.title = element_blank(),
legend.key.size = unit(0.9, 'lines')) +
scale_y_log10(breaks = seq(0, 350, 50))
Detroit, Houston, San Francisco, Seattle
(ref:A208-A318-A422-A423) Detroit, Houston, San Francisco, Seattle
Code
cpi %>%
filter(variable %in% c("CUURA208SEHA", "CUURA318SEHA", "CUURA422SEHA", "CUURA423SEHA")) %>%
left_join(variable, by = "variable") %>%
filter(year(date) >= 1978) %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers: Rent of Primary Residence in ", "", Variable)) %>%
ggplot + geom_line(aes(x = date, y = value, color = Variable)) +
ylab("CPI - All Urban Consumers: Rent of Primary Residence") + xlab("") +
theme_minimal() +
scale_x_date(breaks = seq(1700, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.90),
legend.title = element_blank(),
legend.key.size = unit(0.9, 'lines')) +
scale_y_log10(breaks = seq(0, 350, 50))
San Francisco
Code
cpi %>%
filter(variable %in% c("CUURA422SEHA", "CUURA422SEHC", "CUURA422SEHC01", "CUURA422SASL2RS")) %>%
left_join(variable, by = "variable") %>%
filter(year(date) >= 1987) %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers: ", "", Variable),
Variable = gsub(" in San Francisco-Oakland-Hayward, CA \\(CBSA\\)", "", Variable)) %>%
ggplot + geom_line(aes(x = date, y = value, color = Variable)) +
ylab("CPI - All Urban Consumers: Rent of Primary Residence") + xlab("") +
theme_minimal() +
scale_x_date(breaks = seq(1700, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.90),
legend.title = element_blank(),
legend.key.size = unit(0.9, 'lines')) +
scale_y_log10(breaks = c(seq(0, 500, 50), 450, 550))
Relative Rent Price
(ref:us-relative-rent-price) Relative Rent Price
Code
cpi %>%
filter(variable %in% c("CUUR0000SEHA", "CPIAUCSL", "CUUR0000SA0L2")) %>%
filter(year(date) >= 1947, month(date) == 1) %>%
spread(variable, value) %>%
mutate(rent_real = 100*CUUR0000SEHA / CPIAUCSL,
rent_real_less_shelter = 100*CUUR0000SEHA / CUUR0000SA0L2) %>%
select(-CUUR0000SEHA, -CPIAUCSL, -CUUR0000SA0L2) %>%
gather(variable, value, -date) %>%
mutate(variable_desc = case_when(variable == "rent_real" ~ "Rent rel. to overall CPI",
variable == "rent_real_less_shelter" ~ "Rent rel. to CPI excluding shelter",
variable == "UNRATE" ~ "Unemployment Rate")) %>%
ggplot + geom_line(aes(x = date, y = value, color = variable_desc)) +
ylab("") + xlab("") +
theme_minimal() +
scale_x_date(breaks = seq(1700, 2025, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.90),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 350, 5))