parameter_sensitivity: Parameter Sensitivity Analysis for DSGE Models

In dsge: Dynamic Stochastic General Equilibrium Models

Parameter Sensitivity Analysis for DSGE Models

Description

Evaluates the sensitivity of key model outputs to one-at-a-time parameter perturbations. For each free parameter, the model is re-solved at theta +/- delta, and changes in the log-likelihood, impulse responses, steady state, and policy matrix are recorded.

Usage

parameter_sensitivity(x, ...)

## S3 method for class 'dsge_fit'
parameter_sensitivity(
  x,
  what = c("loglik", "irf"),
  delta = 0.01,
  irf_horizon = 20L,
  ...
)

## S3 method for class 'dsge_bayes'
parameter_sensitivity(
  x,
  what = c("loglik", "irf"),
  delta = 0.01,
  irf_horizon = 20L,
  ...
)

Arguments

x	A dsge_fit or dsge_bayes object.
...	Additional arguments (currently unused).
what	Character vector specifying which outputs to assess. Any subset of c("loglik", "irf", "steady_state", "policy"). Default is c("loglik", "irf").
delta	Numeric. Perturbation size as a fraction of the parameter value. Default is 0.01 (1 percent).
irf_horizon	Integer. Number of IRF periods. Default is 20.

Details

For each parameter \theta_j, the model is solved at \theta_j (1 + \delta) and \theta_j (1 - \delta). The numerical derivative is approximated as a central difference. Elasticities are reported as (\theta_j / f) \cdot (df / d\theta_j), representing the percentage change in the output for a 1 percent change in the parameter.

Value

An object of class "dsge_sensitivity" containing:

loglik

Data frame of log-likelihood sensitivities (if requested).

irf

Data frame of IRF sensitivities (if requested).

steady_state

Data frame of steady-state sensitivities (if requested).

policy

Data frame of policy matrix sensitivities (if requested).

param_names

Character vector of parameter names.

delta

Perturbation fraction used.

Examples

m <- dsge_model(
  obs(y ~ z),
  state(z ~ rho * z),
  start = list(rho = 0.5)
)
set.seed(1)
z <- numeric(100); for (i in 2:100) z[i] <- 0.8*z[i-1]+rnorm(1)
fit <- estimate(m, data = data.frame(y = z))
sa <- parameter_sensitivity(fit)
print(sa)

dsge documentation built on Sept. 25, 2026, 5:08 p.m.