Inflation Expectations

The Federal Reserve Bank of New York provides interesting data on inflation expectations. I was first interested in this because it appears as though the survey respondents have consistently overestimated inflation expectations over the last few years. But now, in a time of heightened concern about inflation, it is worthwhile to revisit the data.

library(tidyverse)
library(lubridate); library(tsibble)
library(readxl); library(magrittr)
library(kableExtra)
url <- "https://www.newyorkfed.org/medialibrary/interactives/sce/sce/downloads/data/frbny-sce-data.xlsx"
destfile <- "frbny_sce_data.xlsx"
curl::curl_download(url, destfile)
Inflation.Expectations <- read_excel(destfile, sheet=4, skip=3) 
Inflation.Expectations %>% 
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "500px")

Now let me format the dates properly to treat these data as a time series.

Inflation.Expectations %<>%
  rename(date = 1) %>% 
  mutate(date = yearmonth(parse_date_time(date, orders = "%Y%m")))

That’s pretty much all that is required to visualize them. Let’s have a look. I will use the fpp3 library and autoplot().

library(fpp3)

## ── Attaching packages ──────────────────────────────────────────── fpp3 0.4.0 ──

## ✓ tsibbledata 0.3.0     ✓ fable       0.3.1
## ✓ feasts      0.2.2

## ── Conflicts ───────────────────────────────────────────────── fpp3_conflicts ──
## x lubridate::date()        masks base::date()
## x magrittr::extract()      masks tidyr::extract()
## x dplyr::filter()          masks stats::filter()
## x kableExtra::group_rows() masks dplyr::group_rows()
## x tsibble::intersect()     masks base::intersect()
## x tsibble::interval()      masks lubridate::interval()
## x dplyr::lag()             masks stats::lag()
## x magrittr::set_names()    masks purrr::set_names()
## x tsibble::setdiff()       masks base::setdiff()
## x tsibble::union()         masks base::union()

Inflation.Expectations %>% 
  as_tsibble(index=date) %>% 
  autoplot(`Median three-year ahead expected inflation rate`) + 
  geom_line(aes(y=Inflation.Expectations$`Median one-year ahead expected inflation rate`, color="red")) +
  hrbrthemes::theme_ipsum() + guides(color = "none")

Now let me pretty the plot.

Inflation.Expectations %>% 
  select(1:3) %>% 
  pivot_longer(c(2:3)) %>%
  mutate(Variable = name) %>%
  ggplot(aes(x=date, y=value, color=Variable)) +
  geom_line(size=2) +
  scale_color_viridis_d(option="C") +
  hrbrthemes::theme_ft_rc() +
  theme(legend.position = "bottom") +
  labs(y="Inflation Expectations", 
       color="",
       title="New York Fed Inflation Expectations",
       caption = "data: https://www.newyorkfed.org/medialibrary/interactives/sce/sce/downloads/data/frbny-sce-data.xlsx")