Inflation

Data - Fred

Info

source dataset .html .RData
fred inflation 2024-12-22 2024-12-22

Data on inflation

source dataset .html .RData
bis CPI 2024-07-01 2022-01-20
ecb CES 2024-12-21 2024-12-21
eurostat nama_10_co3_p3 2024-12-14 2024-12-14
eurostat prc_hicp_cow 2024-11-22 2024-10-08
eurostat prc_hicp_ctrb 2024-11-22 2024-10-08
eurostat prc_hicp_inw 2024-11-05 2024-11-23
eurostat prc_hicp_manr 2024-12-21 2024-12-21
eurostat prc_hicp_midx 2024-11-01 2024-12-22
eurostat prc_hicp_mmor 2024-11-22 2024-11-23
eurostat prc_ppp_ind 2024-11-22 2024-10-08
eurostat sts_inpp_m 2024-06-24 2024-12-21
eurostat sts_inppd_m 2024-12-21 2024-12-21
eurostat sts_inppnd_m 2024-06-24 2024-12-16
fred cpi 2024-12-22 2024-12-22
fred inflation 2024-12-22 2024-12-22
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
2024-12-22

Last

date Nobs
2024-12-20 3

variable

variable Variable Nobs
T10YIE 10-Year Breakeven Inflation Rate 5732
T5YIE 5-Year Breakeven Inflation Rate 5732
T5YIFR 5-Year, 5-Year Forward Inflation Expectation Rate 5732
GASREGW US Regular All Formulations Gas Price 1792
CUUR0000SA0L2 Consumer Price Index for All Urban Consumers: All Items Less Shelter in U.S. City Average 1077
CPIAUCSL Consumer Price Index for All Urban Consumers: All Items in U.S. City Average 935
UNRATE Unemployment Rate 923
CUUR0000SAH1 Consumer Price Index for All Urban Consumers: Shelter in U.S. City Average 864
CPILFESL Consumer Price Index for All Urban Consumers: All Items Less Food and Energy in U.S. City Average 815
PCEPI Personal Consumption Expenditures: Chain-type Price Index 791
PCEPILFE Personal Consumption Expenditures Excluding Food and Energy (Chain-Type Price Index) 791
MICH University of Michigan: Inflation Expectation 563
CP0000USM086NEST Harmonized Index of Consumer Prices: All Items for United States 324
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) 300
TOTNRGFOODEA20MI15XM Harmonized Index of Consumer Prices: Overall Index Excluding Energy, Food, Alcohol, and Tobacco for Euro area (20 countries) 288
T7YIEM 7-year Breakeven Inflation Rate 263
DPCCRV1Q225SBEA Personal Consumption Expenditures (PCE) Excluding Food and Energy (Chain-Type Price Index) 262
T20YIEM 20-year Breakeven Inflation Rate 245
T30YIEM 30-year Breakeven Inflation Rate 178
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
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
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
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))