Inflation
Data - Fred
Info
Data on inflation
| 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 | 2024-11-09 |
| 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 | 2024-11-08 |
| 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 | 2024-11-17 | 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 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-11-21 |
Last
| date | Nobs |
|---|---|
| 2024-11-21 | 3 |
variable
| variable | Variable | Nobs |
|---|---|---|
| T10YIE | 10-Year Breakeven Inflation Rate | 5711 |
| T5YIE | 5-Year Breakeven Inflation Rate | 5711 |
| T5YIFR | 5-Year, 5-Year Forward Inflation Expectation Rate | 5711 |
| GASREGW | US Regular All Formulations Gas Price | 1788 |
| CUUR0000SA0L2 | Consumer Price Index for All Urban Consumers: All Items Less Shelter in U.S. City Average | 1076 |
| CPIAUCSL | Consumer Price Index for All Urban Consumers: All Items in U.S. City Average | 934 |
| UNRATE | Unemployment Rate | 922 |
| CUUR0000SAH1 | Consumer Price Index for All Urban Consumers: Shelter in U.S. City Average | 863 |
| CPILFESL | Consumer Price Index for All Urban Consumers: All Items Less Food and Energy in U.S. City Average | 814 |
| PCEPI | Personal Consumption Expenditures: Chain-type Price Index | 789 |
| PCEPILFE | Personal Consumption Expenditures Excluding Food and Energy (Chain-Type Price Index) | 789 |
| MICH | University of Michigan: Inflation Expectation | 561 |
| CP0000USM086NEST | Harmonized Index of Consumer Prices: All Items for United States | 323 |
| A255RD3Q086SBEA | Imports of goods (implicit price deflator) | 311 |
| GDPDEF | Gross Domestic Product: Implicit Price Deflator | 311 |
| CP00MI15EA20M086NEST | Harmonized Index of Consumer Prices: All-items HICP for Euro area (20 countries) | 299 |
| TOTNRGFOODEA20MI15XM | Harmonized Index of Consumer Prices: Overall Index Excluding Energy, Food, Alcohol, and Tobacco for Euro area (20 countries) | 287 |
| DPCCRV1Q225SBEA | Personal Consumption Expenditures (PCE) Excluding Food and Energy (Chain-Type Price Index) | 262 |
| T7YIEM | 7-year Breakeven Inflation Rate | 262 |
| T20YIEM | 20-year Breakeven Inflation Rate | 244 |
| T30YIEM | 30-year Breakeven Inflation Rate | 177 |
| FPCPITOTLZGUSA | Inflation, consumer prices for the United States | 64 |
CPI, HICP
All
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "CP00MI15EA20M086NEST")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
mutate(country = ifelse(variable == "CP00MI15EA20M086NEST", "Euro area (HICP)", "US (CPI)")) %>%
na.omit %>%
#filter(value >= 0.14) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country)) + theme_minimal() +
scale_x_date(breaks = "5 years",
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.2, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_hline(yintercept = 0.02, linetype = "dashed")
Since 2020
All
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "CP00MI15EA20M086NEST")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
filter(date >= as.Date("2009-01-01")) %>%
mutate(country = ifelse(variable == "CP00MI15EA20M086NEST", "Euro area (HICP)", "US (CPI)")) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country)) + theme_minimal() +
scale_x_date(breaks = "1 year",
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.2, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_hline(yintercept = 0.02, linetype = "dashed")
CPI, PCE, HICP
Since 2021
Inflation (1st difference, 1 year)
English
Code
Sys.setlocale("LC_TIME", "en_CA.UTF-8")# [1] "en_CA.UTF-8"Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "CP0000USM086NEST", "CP00MI15EA20M086NEST")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
filter(date >= as.Date("2020-02-01")) %>%
mutate(country = ifelse(variable == "CP00MI15EA20M086NEST", "Euro area", "US"),
type = case_when(variable %in% c("CP00MI15EA20M086NEST", "CP0000USM086NEST") ~ "HICP, Proxy-HICP",
variable == "CPIAUCSL" ~ "CPI",
variable == "PCEPI" ~ "PCE"),
type = factor(type,
levels = c("HICP, Proxy-HICP", "CPI", "PCE"),
labels = c("Harmonized Index of Consumer Prices (HICP)",
"Consumer Price Index, BLS (CPI)",
"Personal Consumption Expenditures, BEA (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "2 months"),
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.25, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"), as.Date("2022-10-01"))),
aes(x = date, y = value, label = percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3) +
geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") +
geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")
Since 2020
Inflation (1st difference, 1 year)
English
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "CP0000USM086NEST", "CP00MI15EA20M086NEST")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
filter(date >= as.Date("2020-01-01")) %>%
mutate(country = ifelse(variable == "CP00MI15EA20M086NEST", "Euro area", "US"),
type = case_when(variable %in% c("CP00MI15EA20M086NEST", "CP0000USM086NEST") ~ "Harmonized Index of Consumer Prices (HICP, Proxy-HICP)",
variable == "CPIAUCSL" ~ "Consumer Price Index, BLS (CPI)",
variable == "PCEPI" ~ "Personal Consumption Expenditures, BEA (PCE)"),
type = factor(type, levels = c("Harmonized Index of Consumer Prices (HICP, Proxy-HICP)",
"Consumer Price Index, BLS (CPI)",
"Personal Consumption Expenditures, BEA (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "2 months"),
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.3, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"), as.Date("2022-10-01"))),
aes(x = date, y = value, label = percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3) +
geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") +
geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")
French
Code
Sys.setlocale("LC_TIME", "fr_CA.UTF-8")# [1] "fr_CA.UTF-8"Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "CP0000USM086NEST", "CP00MI15EA20M086NEST")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
filter(date >= as.Date("2020-01-01")) %>%
mutate(country = ifelse(variable == "CP00MI15EA20M086NEST", "Zone euro", "États-Unis"),
country = factor(country, levels = c("Zone euro", "États-Unis")),
type = case_when(variable %in% c("CP00MI15EA20M086NEST", "CP0000USM086NEST") ~ "Indice des Prix à la Consommation Harmonisé (IPCH, Proxy-IPCH)",
variable == "CPIAUCSL" ~ "Indice des Prix à la Consommation (CPI)",
variable == "PCEPI" ~ "Déflateur de la Consommation (PCE)"),
type = factor(type, levels = c("Indice des Prix à la Consommation Harmonisé (IPCH, Proxy-IPCH)",
"Indice des Prix à la Consommation (CPI)",
"Déflateur de la Consommation (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, linetype = type, color = country)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "3 months"),
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Inflation sur un an (%)") +
theme(legend.position = c(0.3, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date %in% c(as.Date("2022-06-01"), as.Date("2022-10-01"))),
aes(x = date, y = value, label = percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3) +
geom_text_repel(data = . %>% filter(date %in% c(max(date))),
aes(x = date, y = value, label = percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3) +
geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") +
geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted") +
guides(linetype = guide_legend(order = 1),
color = guide_legend(order = 2))
Inflation, 1st difference
1 month
Code
Sys.setlocale("LC_TIME", "en_CA.UTF-8")# [1] "en_CA.UTF-8"Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "CP0000USM086NEST", "CP00MI15EA20M086NEST")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 1) - 1) %>%
filter(date >= as.Date("2020-01-01")) %>%
mutate(country = ifelse(variable == "CP00MI15EA20M086NEST", "Euro area", "U.S."),
type = case_when(variable %in% c("CP00MI15EA20M086NEST", "CP0000USM086NEST") ~ "Harmonized Index of Consumer Prices (HICP, Proxy-HICP)",
variable == "CPIAUCSL" ~ "Consumer Price Index, BLS (CPI)",
variable == "PCEPI" ~ "Personal Consumption Expenditures, BEA (PCE)"),
type = factor(type, levels = c("Harmonized Index of Consumer Prices (HICP, Proxy-HICP)",
"Consumer Price Index, BLS (CPI)",
"Personal Consumption Expenditures, BEA (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "2 months"),
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Inflation, 1 month (%)") +
theme(legend.position = c(0.3, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"), as.Date("2022-10-01"))),
aes(x = date, y = value, label = percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3) +
geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") +
geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")
2 months
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "CP0000USM086NEST", "CP00MI15EA20M086NEST")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 2) - 1) %>%
filter(date >= as.Date("2020-01-01")) %>%
mutate(country = ifelse(variable == "CP00MI15EA20M086NEST", "Euro area", "U.S."),
type = case_when(variable %in% c("CP00MI15EA20M086NEST", "CP0000USM086NEST") ~ "Harmonized Index of Consumer Prices (HICP, Proxy-HICP)",
variable == "CPIAUCSL" ~ "Consumer Price Index, BLS (CPI)",
variable == "PCEPI" ~ "Personal Consumption Expenditures, BEA (PCE)"),
type = factor(type, levels = c("Harmonized Index of Consumer Prices (HICP, Proxy-HICP)",
"Consumer Price Index, BLS (CPI)",
"Personal Consumption Expenditures, BEA (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "2 months"),
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Inflation, 2 months (%)") +
theme(legend.position = c(0.3, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"), as.Date("2022-10-01"))),
aes(x = date, y = value, label = percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3) +
geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") +
geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")
3 months
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "CP0000USM086NEST", "CP00MI15EA20M086NEST")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 3) - 1) %>%
filter(date >= as.Date("2020-01-01")) %>%
mutate(country = ifelse(variable == "CP00MI15EA20M086NEST", "Euro area", "U.S."),
type = case_when(variable %in% c("CP00MI15EA20M086NEST", "CP0000USM086NEST") ~ "Harmonized Index of Consumer Prices (HICP, Proxy-HICP)",
variable == "CPIAUCSL" ~ "Consumer Price Index, BLS (CPI)",
variable == "PCEPI" ~ "Personal Consumption Expenditures, BEA (PCE)"),
type = factor(type, levels = c("Harmonized Index of Consumer Prices (HICP, Proxy-HICP)",
"Consumer Price Index, BLS (CPI)",
"Personal Consumption Expenditures, BEA (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "2 months"),
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Inflation, 3 months (%)") +
theme(legend.position = c(0.3, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"), as.Date("2022-10-01"))),
aes(x = date, y = value, label = percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3) +
geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") +
geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")
6 months
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "CP0000USM086NEST", "CP00MI15EA20M086NEST")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 6) - 1) %>%
filter(date >= as.Date("2020-01-01")) %>%
mutate(country = ifelse(variable == "CP00MI15EA20M086NEST", "Euro area", "U.S."),
type = case_when(variable %in% c("CP00MI15EA20M086NEST", "CP0000USM086NEST") ~ "Harmonized Index of Consumer Prices (HICP, Proxy-HICP)",
variable == "CPIAUCSL" ~ "Consumer Price Index, BLS (CPI)",
variable == "PCEPI" ~ "Personal Consumption Expenditures, BEA (PCE)"),
type = factor(type, levels = c("Harmonized Index of Consumer Prices (HICP, Proxy-HICP)",
"Consumer Price Index, BLS (CPI)",
"Personal Consumption Expenditures, BEA (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "2 months"),
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Inflation, 6 months (%)") +
theme(legend.position = c(0.3, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"), as.Date("2022-10-01"))),
aes(x = date, y = value, label = percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3) +
geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") +
geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")
Price Index
English
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "CP0000USM086NEST", "CP00MI15EA20M086NEST")) %>%
add_row(date = as.Date("2023-10-01"), variable = "CP00MI15EA20M086NEST", value = 124.55) %>%
select(date, variable, value) %>%
filter(date >= as.Date("2020-01-01")) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
mutate(country = ifelse(variable == "CP00MI15EA20M086NEST", "Euro area", "US"),
type = case_when(variable %in% c("CP00MI15EA20M086NEST", "CP0000USM086NEST") ~ "Harmonized Index of Consumer Prices (HICP, Proxy-HICP)",
variable == "CPIAUCSL" ~ "Consumer Price Index, BLS (CPI)",
variable == "PCEPI" ~ "Personal Consumption Expenditures, BEA (PCE)"),
type = factor(type, levels = c("Harmonized Index of Consumer Prices (HICP, Proxy-HICP)",
"Consumer Price Index, BLS (CPI)",
"Personal Consumption Expenditures, BEA (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "2 months"),
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = seq(100, 200, 5)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Price Index (January 2020 = 100)") +
theme(legend.position = c(0.3, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
French
Code
Sys.setlocale("LC_TIME", "fr_CA.UTF-8")# [1] "fr_CA.UTF-8"Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "CP0000USM086NEST", "CP00MI15EA20M086NEST")) %>%
add_row(date = as.Date("2023-10-01"), variable = "CP00MI15EA20M086NEST", value = 124.55) %>%
select(date, variable, value) %>%
filter(date >= as.Date("2020-01-01")) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
mutate(country = ifelse(variable == "CP00MI15EA20M086NEST", "Zone euro", "États-Unis"),
country = factor(country, levels = c("Zone euro", "États-Unis")),
type = case_when(variable %in% c("CP00MI15EA20M086NEST", "CP0000USM086NEST") ~ "Indice des Prix à la Consommation Harmonisé (IPCH, Proxy-IPCH)",
variable == "CPIAUCSL" ~ "Indice des Prix à la Consommation (CPI)",
variable == "PCEPI" ~ "Déflateur de la Consommation (PCE)"),
type = factor(type, levels = c("Indice des Prix à la Consommation Harmonisé (IPCH, Proxy-IPCH)",
"Indice des Prix à la Consommation (CPI)",
"Déflateur de la Consommation (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "3 months"),
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = seq(100, 200, 5)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Indice des prix (Janvier 2020 = 100)") +
theme(legend.position = c(0.3, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
guides(linetype = guide_legend(order = 1),
color = guide_legend(order = 2))
Code
Sys.setlocale("LC_TIME", "en_CA.UTF-8")# [1] "en_CA.UTF-8"Core CPI, PCE, HICP
Since 2020
Inflation (1st difference)
1 year
Code
inflation %>%
filter(variable %in% c("PCEPILFE", "CPILFESL", "TOTNRGFOODEA20MI15XM")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
filter(date >= as.Date("2020-01-01")) %>%
mutate(country = ifelse(variable == "TOTNRGFOODEA20MI15XM", "Euro area", "U.S."),
type = case_when(variable %in% c("TOTNRGFOODEA20MI15XM") ~ "Harmonized Index of Consumer Prices (HICP)",
variable == "CPILFESL" ~ "Consumer Price Index, BLS (CPI)",
variable == "PCEPILFE" ~ "Personal Consumption Expenditures, BEA (PCE)"),
type = factor(type, levels = c("Harmonized Index of Consumer Prices (HICP)",
"Consumer Price Index, BLS (CPI)",
"Personal Consumption Expenditures, BEA (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "2 months"),
labels = scales::date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Core Inflation (%)") +
theme(legend.position = c(0.25, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"), as.Date("2022-10-01"))),
aes(x = date, y = value, label = scales::percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3) +
geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") +
geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")
6 months
Code
inflation %>%
filter(variable %in% c("PCEPILFE", "CPILFESL", "TOTNRGFOODEA20MI15XM")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = (value/lag(value, 6))^2 - 1) %>%
filter(date >= as.Date("2020-01-01")) %>%
mutate(country = ifelse(variable == "TOTNRGFOODEA20MI15XM", "Euro area", "U.S."),
type = case_when(variable %in% c("TOTNRGFOODEA20MI15XM") ~ "Harmonized Index of Consumer Prices (HICP)",
variable == "CPILFESL" ~ "Consumer Price Index, BLS (CPI)",
variable == "PCEPILFE" ~ "Personal Consumption Expenditures, BEA (PCE)"),
type = factor(type, levels = c("Harmonized Index of Consumer Prices (HICP)",
"Consumer Price Index, BLS (CPI)",
"Personal Consumption Expenditures, BEA (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "2 months"),
labels = scales::date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Core Inflation, 6 months annualized (%)") +
theme(legend.position = c(0.25, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"), as.Date("2022-10-01"))),
aes(x = date, y = value, label = scales::percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
3 months
Code
inflation %>%
filter(variable %in% c("PCEPILFE", "CPILFESL", "TOTNRGFOODEA20MI15XM")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = (value/lag(value, 3))^4 - 1) %>%
filter(date >= as.Date("2020-01-01")) %>%
mutate(country = ifelse(variable == "TOTNRGFOODEA20MI15XM", "Euro area", "U.S."),
type = case_when(variable %in% c("TOTNRGFOODEA20MI15XM") ~ "Harmonized Index of Consumer Prices (HICP)",
variable == "CPILFESL" ~ "Consumer Price Index, BLS (CPI)",
variable == "PCEPILFE" ~ "Personal Consumption Expenditures, BEA (PCE)"),
type = factor(type, levels = c("Harmonized Index of Consumer Prices (HICP)",
"Consumer Price Index, BLS (CPI)",
"Personal Consumption Expenditures, BEA (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "2 months"),
labels = scales::date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Core Inflation, 3 months annualized (%)") +
theme(legend.position = c(0.25, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"), as.Date("2022-10-01"))),
aes(x = date, y = value, label = scales::percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
Price Index
Code
inflation %>%
filter(variable %in% c("PCEPILFE", "CPILFESL", "TOTNRGFOODEA20MI15XM")) %>%
select(date, variable, value) %>%
filter(date >= as.Date("2020-01-01")) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
mutate(country = ifelse(variable == "TOTNRGFOODEA20MI15XM", "Euro area", "U.S."),
type = case_when(variable %in% c("TOTNRGFOODEA20MI15XM") ~ "Harmonized Index of Consumer Prices (HICP)",
variable == "CPILFESL" ~ "Consumer Price Index, BLS (CPI)",
variable == "PCEPILFE" ~ "Personal Consumption Expenditures, BEA (PCE)"),
type = factor(type, levels = c("Harmonized Index of Consumer Prices (HICP)",
"Consumer Price Index, BLS (CPI)",
"Personal Consumption Expenditures, BEA (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "2 months"),
labels = scales::date_format("%b %Y")) +
scale_y_continuous(breaks = seq(100, 200, 5)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Price Index (January 2020 = 100)") +
theme(legend.position = c(0.3, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
Since 2021
Inflation (1st difference)
3 months
Code
inflation %>%
filter(variable %in% c("CPILFESL", "TOTNRGFOODEA20MI15XM")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = (value/lag(value, 3))^4 - 1) %>%
filter(date >= as.Date("2022-01-01")) %>%
mutate(country = ifelse(variable == "TOTNRGFOODEA20MI15XM", "Euro area", "U.S."),
type = case_when(variable %in% c("TOTNRGFOODEA20MI15XM") ~ "Harmonized Index of Consumer Prices (HICP)",
variable == "CPILFESL" ~ "Consumer Price Index, BLS (CPI)",
variable == "PCEPILFE" ~ "Personal Consumption Expenditures, BEA (PCE)"),
type = factor(type, levels = c("Harmonized Index of Consumer Prices (HICP)",
"Consumer Price Index, BLS (CPI)",
"Personal Consumption Expenditures, BEA (PCE)"))) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = country)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2020-01-01"), to = Sys.Date(), by = "2 months"),
labels = scales::date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
scale_color_manual(values = c("#003399", "#B22234")) +
xlab("") + ylab("Core Inflation, 3 months annualized (%)") +
theme(legend.position = c(0.25, 0.78),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = scales::percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
CPi, PCE, PCE without energy and food
2010-
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "PCEPILFE", "CPILFESL"),
date >= as.Date("2010-01-01")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = ifelse(variable == "UNRATE", value/100, value/lag(value, 12) - 1)) %>%
left_join(variable, by = "variable") %>%
filter(date >= as.Date("2011-01-01")) %>%
na.omit %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers", "CPI", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2030, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.4, 0.85),
legend.title = element_blank())
2019-
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "PCEPILFE", "CPILFESL"),
date >= as.Date("2010-01-01")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = ifelse(variable == "UNRATE", value/100, value/lag(value, 12) - 1)) %>%
left_join(variable, by = "variable") %>%
filter(date >= as.Date("2019-01-01")) %>%
na.omit %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers", "CPI", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2030, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.4, 0.85),
legend.title = element_blank())
Since 2020
All
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "PCEPILFE", "CPILFESL")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
filter(date >= as.Date("2020-01-01")) %>%
left_join(variable, by = "variable") %>%
na.omit %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers", "CPI", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + theme_minimal() +
scale_x_date(breaks = "3 months",
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.4, 0.8),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
New
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "PCEPILFE", "CPILFESL")) %>%
mutate(category = case_when(variable %in% c("PCEPI", "CPIAUCSL") ~ "All items (Headline)",
T ~ "All items less Food and Energy (Core)"),
PCE_or_CPI = case_when(variable %in% c("CPIAUCSL", "CPILFESL") ~ "Consumer Price Index (CPI)",
T ~ "Personal Consumption Expenditures (PCE)")) %>%
select(date, variable, value, category, PCE_or_CPI) %>%
group_by(category, PCE_or_CPI) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
filter(date >= as.Date("2020-01-01")) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = PCE_or_CPI, linetype = category)) + theme_minimal() +
scale_x_date(breaks = "3 months",
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("US Inflation (%)") +
theme(legend.position = c(0.25, 0.8),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
With tickers
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "PCEPILFE", "CPILFESL")) %>%
mutate(category = case_when(variable %in% c("PCEPI", "CPIAUCSL") ~ "All items (Headline)",
T ~ "All items less Food and Energy (Core)"),
PCE_or_CPI = case_when(variable %in% c("CPIAUCSL", "CPILFESL") ~ "Consumer Price Index (CPI)",
T ~ "Personal Consumption Expenditures (PCE)")) %>%
select(date, variable, value, category, PCE_or_CPI) %>%
group_by(category, PCE_or_CPI) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
filter(date >= as.Date("2020-01-01")) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = PCE_or_CPI, linetype = category)) + theme_minimal() +
scale_x_date(breaks = seq.Date(from = as.Date("2018-06-01"), to = Sys.Date(), by = "3 months"),
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("US Inflation (%)") +
theme(legend.position = c(0.25, 0.8),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"))),
aes(x = date, y = value, label = percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3) +
geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted")
2 years
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "PCEPI", "PCEPILFE", "CPILFESL")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
filter(date >= Sys.Date() - years(2)) %>%
left_join(variable, by = "variable") %>%
na.omit %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers", "CPI", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + theme_minimal() +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.4, 0.15),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
as.Date("2022-01-01"), as.Date("2023-02-01"))),
aes(x = date, y = value, label = percent(value, acc = 0.1)),
fontface ="plain", color = "black", size = 3) +
geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") +
geom_vline(xintercept = as.Date("2022-01-01"), linetype = "dotted") +
geom_vline(xintercept = as.Date("2023-02-01"), linetype = "dotted")
Rents,Without Rents, general, unemployment
2010-
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "CUUR0000SA0L2", "CUUR0000SAH1", "UNRATE"),
date >= as.Date("2010-01-01")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = ifelse(variable == "UNRATE", value/100, value/lag(value, 12) - 1)) %>%
left_join(variable, by = "variable") %>%
filter(date >= as.Date("2011-01-01")) %>%
na.omit %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers", "CPI", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2030, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation (%), Unemployment Rate (%)") +
theme(legend.position = c(0.4, 0.85),
legend.title = element_blank())
2 years
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "CUUR0000SA0L2", "CUUR0000SAH1", "UNRATE")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = ifelse(variable == "UNRATE", value/100, value/lag(value, 12) - 1)) %>%
filter(date >= Sys.Date() - years(2)) %>%
left_join(variable, by = "variable") %>%
na.omit %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers", "CPI", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + theme_minimal() +
scale_x_date(breaks = "2 months",
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation (%), Unemployment Rate (%)") +
theme(legend.position = c(0.3, 0.15),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
Rents,Without Rents VS general
2010-
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "CUUR0000SA0L2", "CUUR0000SAH1"),
date >= as.Date("2010-01-01")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
left_join(variable, by = "variable") %>%
na.omit %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers", "CPI", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2030, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
2 years
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "CUUR0000SA0L2", "CUUR0000SAH1")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
filter(date >= Sys.Date() - years(3)) %>%
left_join(variable, by = "variable") %>%
na.omit %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers", "CPI", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + theme_minimal() +
scale_x_date(breaks = "2 months",
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.5, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
Without Rents VS general
All
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "CUUR0000SA0L2"),
date >= as.Date("1947-01-01")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
left_join(variable, by = "variable") %>%
na.omit %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers", "CPI", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.7, 0.9),
legend.title = element_blank())
2000-
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "CUUR0000SA0L2"),
date >= as.Date("2000-01-01")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
left_join(variable, by = "variable") %>%
na.omit %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers", "CPI", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2030, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
2010-
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "CUUR0000SA0L2"),
date >= as.Date("2010-01-01")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
left_join(variable, by = "variable") %>%
na.omit %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers", "CPI", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2030, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
2 years
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "CUUR0000SA0L2")) %>%
select(date, variable, value) %>%
group_by(variable) %>%
arrange(date) %>%
mutate(value = value/lag(value, 12) - 1) %>%
filter(date >= Sys.Date() - years(3)) %>%
left_join(variable, by = "variable") %>%
na.omit %>%
mutate(Variable = gsub("Consumer Price Index for All Urban Consumers", "CPI", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + theme_minimal() +
scale_x_date(breaks = "2 months",
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation (%)") +
theme(legend.position = c(0.5, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
Inflation U.S.
Consumer Inflation Expectations - MICH
All
Code
inflation %>%
filter(variable %in% c("MICH")) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Michigan Consumper Expectations (%)") +
scale_color_manual(values = c("#2D68C4", "#F2A900", "#000000")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
1985-
Code
inflation %>%
filter(variable %in% c("MICH")) %>%
left_join(variable, by = "variable") %>%
filter(date >= as.Date("1985-01-01")) %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Michigan Consumper Expectations (%)") +
scale_color_manual(values = c("#2D68C4", "#F2A900", "#000000")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
1995-
Code
inflation %>%
filter(variable %in% c("MICH")) %>%
left_join(variable, by = "variable") %>%
filter(date >= "1995-01-01") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Michigan Consumper Expectations (%)") +
scale_color_manual(values = c("#2D68C4", "#F2A900", "#000000")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
CPI, PCE, A255RD3Q086SBEA
1945-1970
Same Scale
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "A255RD3Q086SBEA"),
month(date) == 1,
date >= as.Date("1945-01-01"),
date <= as.Date("1970-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
A255RD3Q086SBEA = 100*(log(A255RD3Q086SBEA) - lag(log(A255RD3Q086SBEA)))) %>%
gather(variable, value, -date) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation Rates (%)") +
scale_color_manual(values = c("#2D68C4", "#F2A900", "#000000")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
Different Scale
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "A255RD3Q086SBEA"),
month(date) == 1,
date >= as.Date("1945-01-01"),
date <= as.Date("1970-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
A255RD3Q086SBEA = 20*(log(A255RD3Q086SBEA) - lag(log(A255RD3Q086SBEA)))) %>%
gather(variable, value, -date) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1),
limits = c(-0.035, 0.16)) +
xlab("") + ylab("Inflation Rates (%) - CPI, Imports of goods/5") +
scale_color_manual(values = c("#2D68C4", "#F2A900")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
1945-
Code
inflation %>%
filter(variable %in% c("DPCCRV1Q225SBEA", "CPIAUCSL", "A255RD3Q086SBEA"),
month(date) == 1,
date >= as.Date("1945-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
A255RD3Q086SBEA = 100*(log(A255RD3Q086SBEA) - lag(log(A255RD3Q086SBEA)))) %>%
gather(variable, value, -date) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation Rates (%)") +
scale_color_manual(values = c("#2D68C4", "#F2A900", "#000000")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
1960-
Same Scale
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "A255RD3Q086SBEA"),
month(date) == 1,
date >= as.Date("1960-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
A255RD3Q086SBEA = 100*(log(A255RD3Q086SBEA) - lag(log(A255RD3Q086SBEA)))) %>%
gather(variable, value, -date) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation Rates (%)") +
scale_color_manual(values = c("#2D68C4", "#F2A900")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
Different Scale
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "A255RD3Q086SBEA"),
month(date) == 1,
date >= as.Date("1960-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
A255RD3Q086SBEA = 20*(log(A255RD3Q086SBEA) - lag(log(A255RD3Q086SBEA)))) %>%
gather(variable, value, -date) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1),
limits = c(-0.035, 0.16)) +
xlab("") + ylab("Inflation Rates (%) - CPI, Imports of goods/5") +
scale_color_manual(values = c("#2D68C4", "#F2A900")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
1995-
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "A255RD3Q086SBEA"),
month(date) == 1,
date >= as.Date("1995-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
A255RD3Q086SBEA = 20*(log(A255RD3Q086SBEA) - lag(log(A255RD3Q086SBEA)))) %>%
gather(variable, value, -date) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1),
limits = c(-0.035, 0.06)) +
xlab("") + ylab("Inflation Rates (%) - CPI, Imports of goods/5") +
scale_color_manual(values = c("#2D68C4", "#F2A900")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
1990-2005
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "A255RD3Q086SBEA"),
month(date) == 1,
date >= as.Date("1990-01-01"),
date <= as.Date("2005-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
A255RD3Q086SBEA = 20*(log(A255RD3Q086SBEA) - lag(log(A255RD3Q086SBEA)))) %>%
gather(variable, value, -date) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1),
limits = c(-0.035, 0.06)) +
xlab("") + ylab("Inflation Rates (%) - CPI, Imports of goods/5") +
scale_color_manual(values = c("#2D68C4", "#F2A900")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
1960-1980
Same scale
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "A255RD3Q086SBEA"),
month(date) == 1,
date >= as.Date("1960-01-01"),
date <= as.Date("1980-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
A255RD3Q086SBEA = 100*(log(A255RD3Q086SBEA) - lag(log(A255RD3Q086SBEA)))) %>%
gather(variable, value, -date) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation Rates (%) - CPI, Imports of goods") +
scale_color_manual(values = c("#2D68C4", "#F2A900")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
Scale divided by 5
Code
inflation %>%
filter(variable %in% c("CPIAUCSL", "A255RD3Q086SBEA"),
month(date) == 1,
date >= as.Date("1960-01-01"),
date <= as.Date("1980-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
A255RD3Q086SBEA = 20*(log(A255RD3Q086SBEA) - lag(log(A255RD3Q086SBEA)))) %>%
gather(variable, value, -date) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Inflation Rates (%) - CPI, Imports of goods/5") +
scale_color_manual(values = c("#2D68C4", "#F2A900")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
Inflation
Code
inflation %>%
filter(variable %in% c("DPCCRV1Q225SBEA")) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 15, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Breakeven Inflation Rates (%)") +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank())
AFGAP Graph
All
- Association Française des Gestionnaires Actif-Passif - AFGAP. pdf
Code
inflation %>%
filter(variable %in% c("DPCCRV1Q225SBEA", "CPIAUCSL"),
month(date) == 1,
date >= as.Date("1960-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL)))) %>%
gather(variable, value, -date) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 18, 1),
labels = scales::percent_format(accuracy = 1),
limits = c(-0.01, 0.18)) +
xlab("") + ylab("Inflation Rates (%)") +
scale_color_manual(values = c("#2D68C4", "#F2A900")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank())
US-Presidents
Code
inflation %>%
filter(variable %in% c("DPCCRV1Q225SBEA", "CPIAUCSL"),
month(date) == 1,
date >= as.Date("1960-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL)))) %>%
gather(variable, value, -date) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 18, 1),
labels = scales::percent_format(accuracy = 1),
limits = c(-0.02, 0.16)) +
xlab("") + ylab("Inflation Rates (%)") +
scale_color_manual(values = c("#2D68C4", "#F2A900")) +
geom_vline(aes(xintercept = as.numeric(start)),
data = US_presidents %>%
filter(start >= as.Date("1960-01-01")) %>%
select(-party),
colour = "grey50", alpha = 0.5) +
geom_rect(aes(xmin = start, xmax = end, fill = party),
ymin = -Inf, ymax = Inf, alpha = 0.1,
data = US_presidents %>%
filter(start >= as.Date("1960-01-01"))) +
geom_text(aes(x = start, y = new, label = name),
data = US_presidents %>%
filter(start >= as.Date("1960-01-01")) %>%
mutate(new = -0.01 + 0.005 * (1:n() %% 2)) %>%
select(-party),
size = 2.5, vjust = 0, hjust = 0, nudge_x = 50) +
scale_fill_manual(values = c("white", "grey")) +
theme(legend.position = c(0.5, 1),
legend.title = element_blank())
Regressions
All
Code
fit1 <- inflation %>%
filter(variable %in% c("CPIAUCSL", "A255RD3Q086SBEA"),
month(date) == 1) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
A255RD3Q086SBEA = 100*(log(A255RD3Q086SBEA) - lag(log(A255RD3Q086SBEA)))) %>%
lm(CPIAUCSL ~ A255RD3Q086SBEA, data = .)
summ(fit1)| Observations | 77 (1 missing obs. deleted) |
| Dependent variable | CPIAUCSL |
| Type | OLS linear regression |
| F(1,75) | 78.70 |
| R² | 0.51 |
| Adj. R² | 0.51 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 2.81 | 0.24 | 11.82 | 0.00 |
| A255RD3Q086SBEA | 0.25 | 0.03 | 8.87 | 0.00 |
| Standard errors: OLS |
1945-1970
Code
fit2 <- inflation %>%
filter(variable %in% c("CPIAUCSL", "A255RD3Q086SBEA"),
month(date) == 1,
date >= as.Date("1945-01-01"),
date <= as.Date("1970-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
A255RD3Q086SBEA = 100*(log(A255RD3Q086SBEA) - lag(log(A255RD3Q086SBEA)))) %>%
lm(CPIAUCSL ~ A255RD3Q086SBEA, data = .)
summ(fit2)| Observations | 23 (1 missing obs. deleted) |
| Dependent variable | CPIAUCSL |
| Type | OLS linear regression |
| F(1,21) | 31.15 |
| R² | 0.60 |
| Adj. R² | 0.58 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 1.84 | 0.38 | 4.87 | 0.00 |
| A255RD3Q086SBEA | 0.30 | 0.05 | 5.58 | 0.00 |
| Standard errors: OLS |
1970-2020
Code
fit3 <- inflation %>%
filter(variable %in% c("CPIAUCSL", "A255RD3Q086SBEA"),
month(date) == 1,
date >= as.Date("1970-01-01")) %>%
select(date, variable, value) %>%
spread(variable, value) %>%
mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
A255RD3Q086SBEA = 100*(log(A255RD3Q086SBEA) - lag(log(A255RD3Q086SBEA)))) %>%
lm(CPIAUCSL ~ A255RD3Q086SBEA, data = .)
summ(fit3)| Observations | 54 (1 missing obs. deleted) |
| Dependent variable | CPIAUCSL |
| Type | OLS linear regression |
| F(1,52) | 54.13 |
| R² | 0.51 |
| Adj. R² | 0.50 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 3.22 | 0.28 | 11.36 | 0.00 |
| A255RD3Q086SBEA | 0.23 | 0.03 | 7.36 | 0.00 |
| Standard errors: OLS |
Breakeven inflation rates
2017-2020
Code
inflation %>%
filter(variable %in% c("T7YIEM", "T5YIE", "T20YIEM", "T10YIE", "T30YIEM"),
date >= as.Date("2009-01-01")) %>%
left_join(variable, by = "variable") %>%
mutate(Variable = gsub("7-year", " 7-year", Variable),
Variable = gsub("5-Year", " 5-year", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = seq(1870, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 15, 0.5),
labels = scales::percent_format(accuracy = .1)) +
xlab("") + ylab("Breakeven Inflation Rates (%)") +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank())
2019-2020
Code
inflation %>%
filter(variable %in% c("T7YIEM", "T5YIE", "T20YIEM", "T10YIE", "T30YIEM"),
date >= as.Date("2019-01-01")) %>%
left_join(variable, by = "variable") %>%
mutate(Variable = gsub("7-year", " 7-year", Variable),
Variable = gsub("5-Year", " 5-year", Variable)) %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + theme_minimal() +
scale_x_date(breaks = "6 months",
labels = date_format("%b %y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 15, 0.5),
labels = scales::percent_format(accuracy = .1)) +
xlab("") + ylab("Breakeven Inflation Rates (%)") +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank())
Inflation Expectations
T5YIFR - 5-Year, 5-Year Forward Inflation Expectation Rate
This series is a measure of expected inflation (on average) over the five-year period that begins five years from today.
This series is constructed as: (((((1+((BC_10YEAR-TC_10YEAR)/100))10)/((1+((BC_5YEAR-TC_5YEAR)/100))5))^0.2)-1)*100
where BC10_YEAR, TC_10YEAR, BC_5YEAR, and TC_5YEAR are the 10 year and 5 year nominal and inflation adjusted Treasury securities.
Code
inflation %>%
filter(variable %in% c("T5YIFR")) %>%
left_join(variable, by = "variable") %>%
ggplot(.) + geom_line(aes(x = date, y = value / 100)) + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 15, 0.5),
labels = scales::percent_format(accuracy = .1)) +
xlab("") + ylab("5-Year, 5-Year Forward Inflation Expectation Rate (%)") +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))