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Compute core inflation measures

Source: R/core.R
ik_core.Rd

Estimates core (underlying) inflation using one of three standard methods: trimmed mean, weighted median, or exclusion-based. These measures aim to strip out transitory price movements and reveal the persistent trend.

Usage

ik_core(
  data,
  method = c("trimmed_mean", "weighted_median", "exclusion", "asymmetric_trim"),
  trim = 0.08,
  trim_lower = 0.24,
  trim_upper = 0.31,
  exclude = NULL,
  date_col = "date",
  item_col = "item",
  change_col = "price_change",
  weight_col = "weight"
)

Arguments

data

A data.frame containing component-level inflation data.

method

Character. One of "trimmed_mean", "weighted_median", "exclusion", or "asymmetric_trim".

trim

Numeric. Fraction to trim from each tail (for "trimmed_mean" only). Default 0.08 (8% symmetric trim, following the Cleveland Fed).

trim_lower

Numeric. Fraction to trim from the lower tail (for "asymmetric_trim" only). Default 0.24, matching the Dallas Fed trimmed mean PCE.

trim_upper

Numeric. Fraction to trim from the upper tail (for "asymmetric_trim" only). Default 0.31, matching the Dallas Fed trimmed mean PCE.

exclude

Character vector. Items to exclude (for "exclusion" only).

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_core" with elements:

core

data.frame with columns: date, core_inflation.

method

Character. The method used.

trim

Numeric. The trim fraction (if applicable).

exclude

Character vector. Excluded items (if applicable).

headline

data.frame with columns: date, headline.

References

Bryan, M. F. and Cecchetti, S. G. (1993). "The Consumer Price Index as a Measure of Inflation." NBER Working Paper No. 4505.

Bryan, M. F. and Cecchetti, S. G. (1994). "Measuring Core Inflation." In Monetary Policy, University of Chicago Press.

Examples

data <- ik_sample_data("components")

# Trimmed mean (default)
core_tm <- ik_core(data, method = "trimmed_mean")
print(core_tm)
#> 
#> ── Core Inflation Measure ──────────────────────────────────────────────────────
#> • Method: Trimmed Mean (8%)
#> • Mean core inflation: 0.27%
#> • Mean headline inflation: 0.26%
#> • Observations: 120

# Weighted median
core_wm <- ik_core(data, method = "weighted_median")
print(core_wm)
#> 
#> ── Core Inflation Measure ──────────────────────────────────────────────────────
#> • Method: Weighted Median
#> • Mean core inflation: 0.27%
#> • Mean headline inflation: 0.26%
#> • Observations: 120

# Exclusion-based
core_ex <- ik_core(data, method = "exclusion", exclude = c("Food", "Transport"))
print(core_ex)
#> 
#> ── Core Inflation Measure ──────────────────────────────────────────────────────
#> • Method: Exclusion (Food, Transport)
#> • Mean core inflation: 0.26%
#> • Mean headline inflation: 0.26%
#> • Observations: 120