Expected inflation: market economists and union officials

library(tidyverse)
library(readrba)
library(readabs)
library(patchwork)

theme_set(theme_minimal(base_size = 14) +
  theme(
    axis.title = element_blank(),
    axis.line.x = element_line(linewidth = 0.25),
    axis.ticks.x = element_line(linewidth = 0.25),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank()
  ))

inf_exp_raw <- read_rba_seriesid(c(
  "GUNIEXPY",
  "GUNIEXPYY",
  "GMAREXPY",
  "GMAREXPYY"
))

inf_exp <- inf_exp_raw |>
  separate(series,
    c("source", "horizon"),
    sep = " – "
  ) |>
  mutate(
    source = str_remove_all(source, "' inflation expectations"),
    series = "Expected inflation"
  ) |>
  select(date, source, horizon, value)

x_scale <- scale_x_date(
  limits = range(inf_exp$date),
  date_labels = "%Y",
  breaks = seq(
    max(inf_exp$date),
    min(inf_exp$date),
    "-10 years"
  )
)

plot_inf_exp <- inf_exp |>
  ggplot(aes(x = date, y = value, col = source)) +
  geom_line() +
  ggtext::geom_richtext(
    data = tibble(
      label = "<span style='color:#F8766D'>Market economists</span><br><span style='color:#00BFC4'>Union officials</span>",
      x = max(inf_exp$date),
      y = 6,
      horizon = "2-year ahead"
    ),
    aes_all(c("x", "y", "label")),
    inherit.aes = FALSE,
    hjust = 1,
    label.size = 0,
    size = 12 / .pt,
    vjust = 1
  ) +
  facet_wrap(~horizon) +
  x_scale +
  labs(subtitle = "Expected inflation") +
  theme(legend.position = "none")

plot_gap <- inf_exp |>
  pivot_wider(
    names_from = source,
    values_from = value
  ) |>
  mutate(
    value = `Union officials` - `Market economists`,
    series = "Difference in expected inflation"
  ) |>
  select(date, horizon, value, series) |>
  filter(!is.na(value)) |>
  ggplot(aes(x = date, y = value)) +
  geom_line() +
  facet_wrap(~horizon) +
  x_scale +
  labs(subtitle = "Difference in expected inflation")

plot_inf_exp /
  plot_gap +
  plot_annotation(caption = "Source: RBA table G03.")

Curious if you compared this to actual inflation and which group tended to be more accurate e.g., using Brier score or something similar

@qwertytam I’ll leave that as an exercise for the reader!