Compute an inflation diffusion index

Measures the breadth of price increases across CPI components. For each period, computes the (weighted or unweighted) fraction of items with price changes exceeding a threshold. A diffusion index above 0.5 indicates that more than half of items are experiencing above-threshold price increases.

Usage

ik_diffusion(
  data,
  threshold = c("zero", "mean", "target"),
  target = 0.02,
  weighted = TRUE,
  date_col = "date",
  item_col = "item",
  change_col = "price_change",
  weight_col = "weight"
)

Arguments

data

A data.frame containing component-level inflation data.

threshold

Character. The threshold rule: "zero" (price_change > 0), "mean" (above the cross-sectional mean), or "target" (above an annualised target rate).

target

Numeric. Annualised inflation target (for "target" threshold only). Default 0.02 (2%).

weighted

Logical. If TRUE (default), compute the weighted fraction. If FALSE, compute the simple (unweighted) fraction.

date_col

Character. Name of the date column. Default "date".

item_col

Character. Name of the item/component column. Default "item".

change_col

Character. Name of the price change column. Default "price_change".

weight_col

Character. Name of the weight column. Default "weight".

Value

An S3 object of class "ik_diffusion" with elements:

diffusion

data.frame with columns: date, diffusion_index.

threshold

Character. The threshold type used.

weighted

Logical. Whether weights were used.

Examples

data <- ik_sample_data("components")

# Fraction of items with rising prices
d <- ik_diffusion(data, threshold = "zero")
print(d)
#> 
#> ── Inflation Diffusion Index ───────────────────────────────────────────────────
#> • Threshold: Price change > 0
#> • Weighted: TRUE
#> • Mean diffusion: 0.765
#> • Latest diffusion: 0.85
#> • Observations: 120
plot(d)