Estimate a Phillips curve

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
)

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)
#> 
#> ── Phillips Curve Estimation ───────────────────────────────────────────────────
#> • Type: Traditional
#> • Slope estimate: -0.1475 (p = 0.0801) *
#> • R-squared: 0.4342
#> • Observations: 76
plot(pc)