Decompose inflation into sticky and flexible components

Splits CPI components into sticky-price and flexible-price categories based on a user-provided classification, then computes separate weighted inflation measures for each group. This follows the Atlanta Fed methodology.

Usage

ik_sticky_flexible(
  data,
  classification,
  date_col = "date",
  item_col = "item",
  change_col = "price_change",
  weight_col = "weight"
)

Arguments

data

A data.frame containing component-level inflation data.

classification

A named logical vector or a data.frame. If a named logical vector, names correspond to item names and TRUE indicates sticky. If a data.frame, it must have columns item (character) and sticky (logical).

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_sticky_flex" with elements:

result

data.frame with columns: date, sticky, flexible, headline.

classification

Named logical vector mapping items to sticky/flexible.

References

Bils, M. and Klenow, P. J. (2004). "Some Evidence on the Importance of Sticky Prices." Journal of Political Economy, 112(5), 947-985.

Examples

data <- ik_sample_data("components")
# Classify items
class_vec <- c(
  Food = FALSE, Housing = TRUE, Transport = FALSE,
  Clothing = FALSE, Health = TRUE, Education = TRUE,
  Communication = TRUE, Recreation = FALSE,
  Restaurants = TRUE, Other = FALSE
)
sf <- ik_sticky_flexible(data, classification = class_vec)
print(sf)
#> 
#> ── Sticky vs Flexible Price Inflation ──────────────────────────────────────────
#> • Sticky items: 5
#> • Flexible items: 5
#> • Mean sticky inflation: 0.3%
#> • Mean flexible inflation: 0.23%
#> • Correlation: -0.042
#> • Observations: 120
plot(sf)