qpm_counterfactual: Historical counterfactuals

In qpmR: Quarterly Projection Models for Monetary Policy Analysis

Historical counterfactuals

Description

Rewrites history with some shocks switched off or scaled: "what if the central bank had simply followed its rule through 2022?" is the path implied by setting the policy shocks to zero over that window and re-running the model from the same starting point with all other shocks unchanged.

Usage

qpm_counterfactual(fit, shocks, periods = NULL, factor = 0, label = NULL)

## S3 method for class 'qpm_counterfactual'
plot(x, vars = NULL, ...)

Arguments

fit	A qpm_filtration.
shocks	Shocks to modify.
periods	Periods over which to modify them (labels as in the data, or integer indices). Default: the whole sample.
factor	Multiplier applied to the selected shocks; 0 (the default) switches them off entirely, 0.5 halves them.
label	Optional name for the scenario.
x	A qpm_counterfactual.
vars	Variables to plot.
...	Unused.

Details

This differs from qpm_decompose(), which attributes the history that happened; here the history is replayed under a different assumption. The counterfactual is only as good as the model's invariance to the intervention — a Lucas-critique caveat that applies to every exercise of this kind and is worth stating in any write-up.

Value

An object of class qpm_counterfactual holding the actual and counterfactual paths and their difference.

Examples

sol <- qpm_solve(qpm_template("bkl"))
obs <- simulate(sol, nsim = 60, seed = 4, burn = 20)
obs$period <- next_quarters("2010-Q4", 60)
fit <- qpm_filter(sol, obs[, c("period", "pi", "i", "q")])
cf <- qpm_counterfactual(fit, shocks = "eps_i",
                         label = "no policy surprises")
cf
plot(cf, vars = c("pi", "i", "y_gap"))

qpmR documentation built on Sept. 29, 2026, 5:10 p.m.