qpm_estimate: Estimate model parameters (Bayesian or maximum likelihood)

In qpmR: Quarterly Projection Models for Monetary Policy Analysis

Estimate model parameters (Bayesian or maximum likelihood)

Description

Estimates any subset of structural parameters and shock standard deviations from data, using the Kalman-filter likelihood of the solved model. method = "bayes" finds the posterior mode (in transformed, unconstrained space), seeds an adaptive random-walk Metropolis sampler with the inverse Hessian, and returns posterior draws with split R-hat and effective-sample-size diagnostics. method = "mle" uses the same machinery with flat priors on the declared supports.

Usage

qpm_estimate(
  model,
  data,
  priors,
  observables = NULL,
  measurement_error = 0,
  kappa = 1e+06,
  method = c("bayes", "mle"),
  iter = 4000,
  burn = floor(iter/2),
  chains = 2,
  thin = 1,
  seed = NULL,
  verbose = TRUE
)

## S3 method for class 'qpm_estimate'
coef(object, type = c("mean", "mode", "median"), ...)

Arguments

model	A qpm_model.
data	Data frame in levels (as for qpm_filter()).
priors	A priors() declaration. Names are structural parameters or shock names (meaning that shock's sd). Required for method = "bayes"; for "mle" it supplies supports (and is otherwise ignored).
observables, measurement_error, kappa	Passed to the filter.
method	"bayes" (default) or "mle".
iter	MCMC iterations per chain (including burn-in).
burn	Burn-in iterations (default iter/2).
chains	Number of chains (run sequentially).
thin	Keep every thin-th post-burn draw.
seed	Optional RNG seed. The previous state of the random number generator is restored on exit, so a seeded call does not disturb the caller's random stream.
verbose	Report progress with message().
object	A qpm_estimate.
type	Point estimate: posterior "mean", "mode", or "median".
...	Unused.

Details

Draws that violate Blanchard-Kahn (indeterminacy or explosiveness) receive zero posterior weight — the usual truncation of the prior to the determinacy region. Everything without a prior stays calibrated at its current value.

Value

An object of class qpm_estimate: posterior draws (natural units), the mode, acceptance rate, split R-hat and effective sample sizes, and the originating model/data. Use coef() to extract point estimates and apply_estimate() to recalibrate the model.

Examples

m <- qpm_model(variables = vars(x = "x"), shocks = shocks(e),
               equations = eqs(x ~ rho * x[-1] + e),
               params = list(rho = 0.5))
obs <- simulate(qpm_solve(qpm_calibrate(m, rho = 0.8)), nsim = 100, seed = 1)
est <- qpm_estimate(m, obs, priors(rho = beta(0.5, 0.2), e = invgamma(1, 0.3)),
                    iter = 300, chains = 2, seed = 2, verbose = FALSE)
est

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