bayes_smc: Tempered Sequential Monte Carlo Sampler for DSGE Estimation
In dsge: Dynamic Stochastic General Equilibrium Models

bayes_smc

R Documentation

Tempered Sequential Monte Carlo Sampler for DSGE Estimation

Description

Runs a tempered SMC sampler over the DSGE parameter space. At each tempering stage particles are reweighted, optionally resampled, and mutated by random-walk Metropolis kernels. More robust than vanilla RWMH for posteriors with multiple modes or strong banana-shaped nonlinearities; the standard alternative to bayes_dsge.

Usage

bayes_smc(
  model,
  data,
  priors,
  n_particles = 500L,
  n_phi = 30L,
  phi_schedule = c("linear", "quadratic"),
  n_mh = 1L,
  ess_threshold = 0.5,
  scale = NULL,
  seed = NULL
)

Arguments

`model`	A `dsge_model` object.
`data`	Matrix or data frame of observed variables.
`priors`	Named list of `dsge_prior` objects.
`n_particles`	Integer. Number of particles. Default 500.
`n_phi`	Integer. Number of tempering stages. Default 30.
`phi_schedule`	Character. Tempering schedule shape. `"linear"` = uniform on the unit interval (default). `"quadratic"` = tighter spacing near phi = 1 (recommended for sharp posteriors).
`n_mh`	Integer. Number of MH mutation steps per tempering stage. Default 1.
`ess_threshold`	Numeric. Resample when effective sample size falls below `ess_threshold * n_particles`. Default 0.5.
`scale`	Numeric. Mutation proposal scale multiplier on the particle-cloud Cholesky factor. Default `2.38 / sqrt(d)` (Roberts-Gelman-Gilks scaling).
`seed`	Optional integer seed.

Value

An object of class c("dsge_smc","dsge_bayes") with the same field layout as bayes_dsge(). Key fields:

posterior: Array (n_particles x n_par x 1) of final particles, treated as posterior draws.
log_marg_lik: Log-marginal-likelihood estimate from the tempering recursion.
ess_path: Effective sample size at each stage.
acceptance_path: MH acceptance rate at each stage.