Package 'inflationkit' reference manual

Title:	Inflation Decomposition, Core Measures, and Trend Estimation
Description:	Tools for analysing inflation dynamics. Computes weighted contributions of price index components, core inflation measures (trimmed mean, weighted median, exclusion-based) following Bryan and Cecchetti (1994), inflation persistence via sum-of-AR-coefficients, diffusion indices, Phillips curve estimation, breakeven inflation, and trend inflation using the Beveridge-Nelson decomposition and Hodrick-Prescott filter. All functions are pure computation and work with price data from any source.
Authors:	Charles Coverdale [aut, cre]
Maintainer:	Charles Coverdale <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0
Built:	2026-05-30 15:07:06 UTC
Source:	https://github.com/charlescoverdale/inflationkit

Compute breakeven inflation rates

Description

Calculates breakeven inflation as the spread between nominal and real (inflation-linked) bond yields. This provides a market-based measure of inflation expectations.

Usage

ik_breakeven(nominal_yield, real_yield, maturity = NULL)
ik_breakeven(nominal_yield, real_yield, maturity = NULL)

Arguments

nominal_yield

Numeric vector. Nominal bond yields.

real_yield

Numeric vector. Real (inflation-linked) bond yields, same length as nominal_yield.

maturity

Numeric vector or NULL. Bond maturities in years. If provided, must be the same length as nominal_yield.

Details

Note: breakeven inflation is a simplified measure that does not account for inflation risk premium or liquidity premium. It should be interpreted as a rough proxy for inflation expectations, not a precise measure.

Value

An S3 object of class "ik_breakeven" with elements:

breakeven: If maturity is provided, a data.frame with columns: maturity, nominal, real, breakeven. Otherwise, a numeric vector of breakeven rates.
maturity: The maturity vector (or NULL).

Examples

# Breakeven term structure
be <- ik_breakeven(
  nominal_yield = c(4.2, 4.5, 4.8, 5.0),
  real_yield = c(1.8, 2.0, 2.3, 2.5),
  maturity = c(2, 5, 10, 30)
)
print(be)
plot(be)

# Simple breakeven (no maturity)
be2 <- ik_breakeven(nominal_yield = 4.5, real_yield = 2.0)
print(be2)
# Breakeven term structure
be <- ik_breakeven(
  nominal_yield = c(4.2, 4.5, 4.8, 5.0),
  real_yield = c(1.8, 2.0, 2.3, 2.5),
  maturity = c(2, 5, 10, 30)
)
print(be)
plot(be)

# Simple breakeven (no maturity)
be2 <- ik_breakeven(nominal_yield = 4.5, real_yield = 2.0)
print(be2)

Compare multiple core inflation measures

Description

Takes multiple ik_core objects and facilitates side-by-side comparison. Produces summary statistics and a multi-line chart of all measures.

Usage

ik_compare(..., labels = NULL)
ik_compare(..., labels = NULL)

Arguments

...

One or more objects of class "ik_core", as returned by ik_core().

labels

Character vector or NULL. Labels for each measure. If NULL, labels are derived from each object's method name.

Value

An S3 object of class "ik_comparison" with elements:

measures: List of ik_core objects.
labels: Character vector of labels.

Examples

data <- ik_sample_data("components")
core_tm <- ik_core(data, method = "trimmed_mean")
core_wm <- ik_core(data, method = "weighted_median")
core_ex <- ik_core(data, method = "exclusion", exclude = c("Food", "Transport"))

comp <- ik_compare(core_tm, core_wm, core_ex)
print(comp)
plot(comp)
data <- ik_sample_data("components")
core_tm <- ik_core(data, method = "trimmed_mean")
core_wm <- ik_core(data, method = "weighted_median")
core_ex <- ik_core(data, method = "exclusion", exclude = c("Food", "Transport"))

comp <- ik_compare(core_tm, core_wm, core_ex)
print(comp)
plot(comp)

Compute core inflation measures

Description

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"
)
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)

# Weighted median
core_wm <- ik_core(data, method = "weighted_median")
print(core_wm)

# Exclusion-based
core_ex <- ik_core(data, method = "exclusion", exclude = c("Food", "Transport"))
print(core_ex)
data <- ik_sample_data("components")

# Trimmed mean (default)
core_tm <- ik_core(data, method = "trimmed_mean")
print(core_tm)

# Weighted median
core_wm <- ik_core(data, method = "weighted_median")
print(core_wm)

# Exclusion-based
core_ex <- ik_core(data, method = "exclusion", exclude = c("Food", "Transport"))
print(core_ex)

Decompose inflation into weighted component contributions

Description

Computes the weighted contribution of each CPI component to headline inflation. The contribution of item i is weight_i * price_change_i, and headline inflation is the sum of all contributions for each period.

Usage

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

Arguments

data

A data.frame containing component-level inflation data.

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_decomposition" with elements:

contributions: data.frame with columns: date, item, weight, price_change, contribution.
headline: data.frame with columns: date, headline_inflation.

Examples

data <- ik_sample_data("components")
decomp <- ik_decompose(data)
print(decomp)
plot(decomp)
data <- ik_sample_data("components")
decomp <- ik_decompose(data)
print(decomp)
plot(decomp)

Compute an inflation diffusion index

Description

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"
)
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)
plot(d)
data <- ik_sample_data("components")

# Fraction of items with rising prices
d <- ik_diffusion(data, threshold = "zero")
print(d)
plot(d)

Evaluate inflation forecasts

Description

Runs standard forecast evaluation tests. The bias test (Mincer-Zarnowitz) checks whether forecasts are unbiased. The efficiency test (Nordhaus) checks whether forecast errors are autocorrelated. The Diebold-Mariano test compares predictive accuracy of two competing forecasts.

Usage

ik_forecast_eval(
  actual,
  forecast,
  test = c("bias", "efficiency", "dm"),
  forecast2 = NULL,
  horizon = 1L,
  alternative = c("two.sided", "less", "greater")
)
ik_forecast_eval(
  actual,
  forecast,
  test = c("bias", "efficiency", "dm"),
  forecast2 = NULL,
  horizon = 1L,
  alternative = c("two.sided", "less", "greater")
)

Arguments

actual

Numeric vector. Realised inflation values.

forecast

Numeric vector. Forecast values, same length as actual.

test

Character. One of "bias", "efficiency", or "dm".

forecast2

Numeric vector or NULL. Second forecast series (required for "dm" test), same length as actual.

horizon

Integer. Forecast horizon (used for Newey-West bandwidth in the DM test). Default 1.

alternative

Character. Alternative hypothesis for the DM test: "two.sided", "less", or "greater".

Value

An S3 object of class "ik_forecast_eval" with elements:

test: Character. The test performed.
statistic: Numeric. The test statistic.
p_value: Numeric. The p-value.
coefficients: Named numeric vector (for bias and efficiency tests).
conclusion: Character. A plain-language summary of the result.

References

Diebold, F. X. and Mariano, R. S. (1995). "Comparing Predictive Accuracy." Journal of Business and Economic Statistics, 13(3), 253-263.

Examples

set.seed(42)
data <- ik_sample_data("headline")
actual <- data$inflation[5:80]
forecast1 <- data$inflation[4:79] + rnorm(76, 0, 0.2)
forecast2 <- data$inflation[4:79] + rnorm(76, 0, 0.4)

# Mincer-Zarnowitz bias test
bias <- ik_forecast_eval(actual, forecast1, test = "bias")
print(bias)

# Diebold-Mariano test
dm <- ik_forecast_eval(actual, forecast1, test = "dm", forecast2 = forecast2)
print(dm)
set.seed(42)
data <- ik_sample_data("headline")
actual <- data$inflation[5:80]
forecast1 <- data$inflation[4:79] + rnorm(76, 0, 0.2)
forecast2 <- data$inflation[4:79] + rnorm(76, 0, 0.4)

# Mincer-Zarnowitz bias test
bias <- ik_forecast_eval(actual, forecast1, test = "bias")
print(bias)

# Diebold-Mariano test
dm <- ik_forecast_eval(actual, forecast1, test = "dm", forecast2 = forecast2)
print(dm)

Measure inflation persistence

Description

Estimates the degree of persistence in an inflation series using one of three methods: sum of AR coefficients, half-life, or largest autoregressive root.

Usage

ik_persistence(
  x,
  method = c("sum_ar", "half_life", "largest_root"),
  ar_order = NULL,
  max_order = 12L,
  ic = c("bic", "aic")
)
ik_persistence(
  x,
  method = c("sum_ar", "half_life", "largest_root"),
  ar_order = NULL,
  max_order = 12L,
  ic = c("bic", "aic")
)

Arguments

x

Numeric vector. An inflation time series.

method

Character. One of "sum_ar", "half_life", or "largest_root".

ar_order

Integer or NULL. The AR order to fit. If NULL, order is selected automatically using the information criterion specified by ic.

max_order

Integer. Maximum AR order to consider when selecting automatically. Default 12.

ic

Character. Information criterion for order selection: "bic" or "aic". Default "bic".

Value

An S3 object of class "ik_persistence" with elements:

value: Numeric. The persistence measure.
method: Character. The method used.
ar_order: Integer. The AR order fitted.
ar_coefficients: Numeric vector. The estimated AR coefficients.
interpretation: Character. A plain-language interpretation ("High persistence", "Moderate persistence", or "Low persistence").

References

Andrews, D. W. K. and Chen, H.-Y. (1994). "Approximately Median-Unbiased Estimation of Autoregressive Models." Journal of Business and Economic Statistics, 12(2), 187-204.

Marques, C. R. (2004). "Inflation Persistence: Facts or Artefacts?" ECB Working Paper No. 371.

Examples

data <- ik_sample_data("headline")
p <- ik_persistence(data$inflation, method = "sum_ar")
print(p)

p_hl <- ik_persistence(data$inflation, method = "half_life")
print(p_hl)
data <- ik_sample_data("headline")
p <- ik_persistence(data$inflation, method = "sum_ar")
print(p)

p_hl <- ik_persistence(data$inflation, method = "half_life")
print(p_hl)

Estimate a Phillips curve

Description

Fits a Phillips curve relating inflation to an economic slack measure (output gap or unemployment rate). Supports traditional, expectations- augmented, and hybrid specifications.

Usage

ik_phillips(
  inflation,
  slack,
  expectations = NULL,
  type = c("traditional", "expectations_augmented", "hybrid"),
  lags = 4L,
  robust_se = FALSE
)
ik_phillips(
  inflation,
  slack,
  expectations = NULL,
  type = c("traditional", "expectations_augmented", "hybrid"),
  lags = 4L,
  robust_se = FALSE
)

Arguments

inflation

Numeric vector. Inflation rate series.

slack

Numeric vector. Slack measure (output gap or unemployment rate), same length as inflation.

expectations

Numeric vector or NULL. Inflation expectations series, required for "expectations_augmented" and "hybrid" types. Must be the same length as inflation.

type

Character. Phillips curve specification: "traditional", "expectations_augmented", or "hybrid".

lags

Integer. Number of lagged inflation terms to include. Default 4.

robust_se

Logical or character. If FALSE, use OLS standard errors. If TRUE or "HC1", compute HC1 heteroskedasticity-robust standard errors. If "HAC", compute Newey-West heteroskedasticity and autocorrelation consistent standard errors with automatic bandwidth selection (Newey and West, 1994). Default FALSE.

Value

An S3 object of class "ik_phillips" with elements:

coefficients: Named numeric vector of estimated coefficients.
std_errors: Named numeric vector of standard errors.
p_values: Named numeric vector of p-values.
r_squared: Numeric. R-squared of the regression.
type: Character. The Phillips curve type.
slope_estimate: Numeric. The estimated slope on the slack variable.
n_obs: Integer. Number of observations used.
residuals: Numeric vector. Regression residuals.

Examples

data <- ik_sample_data("headline")
pc <- ik_phillips(data$inflation, data$unemployment, type = "traditional")
print(pc)
plot(pc)
data <- ik_sample_data("headline")
pc <- ik_phillips(data$inflation, data$unemployment, type = "traditional")
print(pc)
plot(pc)

Generate sample inflation data

Description

Creates synthetic data for testing and demonstrating inflationkit functions. Two types are available: component-level CPI data and headline macro data.

Usage

ik_sample_data(type = c("components", "headline"))
ik_sample_data(type = c("components", "headline"))

Arguments

type

Character. Either "components" for item-level CPI data with weights and price changes, or "headline" for quarterly macro data with inflation, output gap, and unemployment.

Value

A data.frame. For "components": columns date, item, weight, price_change (120 months, 10 items, 1200 rows). For "headline": columns date, inflation, output_gap, unemployment (80 quarterly observations).

Examples

comp <- ik_sample_data("components")
head(comp)

macro <- ik_sample_data("headline")
head(macro)
comp <- ik_sample_data("components")
head(comp)

macro <- ik_sample_data("headline")
head(macro)

Decompose inflation into sticky and flexible components

Description

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"
)
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)
plot(sf)
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)
plot(sf)

Estimate trend inflation

Description

Extracts the trend component from an inflation series using one of four methods: Hodrick-Prescott filter, Beveridge-Nelson decomposition, exponential smoothing, or centred moving average.

Usage

ik_trend(
  x,
  method = c("hp", "beveridge_nelson", "exponential_smooth", "moving_average"),
  frequency = c("quarterly", "monthly", "annual"),
  lambda = NULL,
  window = NULL
)
ik_trend(
  x,
  method = c("hp", "beveridge_nelson", "exponential_smooth", "moving_average"),
  frequency = c("quarterly", "monthly", "annual"),
  lambda = NULL,
  window = NULL
)

Arguments

x

Numeric vector. An inflation time series.

method

Character. One of "hp", "beveridge_nelson", "exponential_smooth", or "moving_average".

frequency

Character. Data frequency: "quarterly" (default), "monthly", or "annual". Used to set the HP filter lambda (when lambda = NULL) and moving average window (when window = NULL).

lambda

Numeric or NULL. Smoothing parameter for the HP filter. If NULL, defaults based on frequency: 6.25 for annual, 1600 for quarterly (Hodrick and Prescott, 1997), or 14400 for monthly (Backus and Kehoe, 1992).

window

Integer or NULL. Window size for the moving average method. Defaults to 4 for quarterly, 12 for monthly, or 3 for annual.

Value

An S3 object of class "ik_trend" with elements:

trend: Numeric vector. The estimated trend component.
cycle: Numeric vector. The cyclical component (original minus trend).
method: Character. The method used.
lambda: Numeric. HP filter lambda (if applicable).
window: Integer. Moving average window (if applicable).
alpha: Numeric. Exponential smoothing parameter (if applicable).
original: Numeric vector. The original series.

References

Hodrick, R. J. and Prescott, E. C. (1997). "Postwar U.S. Business Cycles: An Empirical Investigation." Journal of Money, Credit and Banking, 29(1), 1-16.

Ravn, M. O. and Uhlig, H. (2002). "On Adjusting the Hodrick-Prescott Filter for the Frequency of Observations." Review of Economics and Statistics, 84(2), 371-376.

Examples

data <- ik_sample_data("headline")
tr <- ik_trend(data$inflation, method = "hp")
print(tr)
plot(tr)

tr_ma <- ik_trend(data$inflation, method = "moving_average", window = 4)
print(tr_ma)
data <- ik_sample_data("headline")
tr <- ik_trend(data$inflation, method = "hp")
print(tr)
plot(tr)

tr_ma <- ik_trend(data$inflation, method = "moving_average", window = 4)
print(tr_ma)

Package 'inflationkit'

Help Index

Compute breakeven inflation rates

Description

Usage

Arguments

Details

Value

Examples

Compare multiple core inflation measures

Description

Usage

Arguments

Value

Examples

Compute core inflation measures

Description

Usage

Arguments

Value

References

Examples

Decompose inflation into weighted component contributions

Description

Usage

Arguments

Value

Examples

Compute an inflation diffusion index

Description

Usage

Arguments

Value

Examples

Evaluate inflation forecasts

Description

Usage

Arguments

Value

References

Examples

Measure inflation persistence

Description

Usage

Arguments

Value

References

Examples

Estimate a Phillips curve

Description

Usage

Arguments

Value

Examples

Generate sample inflation data

Description

Usage

Arguments

Value

Examples

Decompose inflation into sticky and flexible components

Description

Usage

Arguments

Value

References

Examples

Estimate trend inflation

Description

Usage

Arguments

Value

References

Examples