View source: R/compute_regime_probabilities.R
| compute_regime_probabilities.PosteriorBSVARMSH | R Documentation | 
Each of the draws from the posterior estimation of a model is transformed into
a draw from the posterior distribution of the regime probabilities. These represent either
the realisations of the regime indicators, when type = "realized", filtered probabilities,
when type = "filtered", forecasted regime probabilities, when type = "forecasted",
or the smoothed probabilities, when type = "smoothed", .
## S3 method for class 'PosteriorBSVARMSH'
compute_regime_probabilities(
  posterior,
  type = c("realized", "filtered", "forecasted", "smoothed")
)
| posterior | posterior estimation outcome - an object of class 
 | 
| type | one of the values  | 
An object of class PosteriorRegimePr, that is, an MxTxS array with attribute PosteriorRegimePr 
containing S draws of the regime probabilities.
Tomasz Woźniak wozniak.tom@pm.me
Song, Y., and Woźniak, T., (2021) Markov Switching. Oxford Research Encyclopedia of Economics and Finance, Oxford University Press, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/acrefore/9780190625979.013.174")}.
estimate, summary
# upload data
data(us_fiscal_lsuw)
# specify the model and set seed
set.seed(123)
specification  = specify_bsvar_msh$new(us_fiscal_lsuw, p = 2, M = 2)
# run the burn-in
burn_in        = estimate(specification, 10)
# estimate the model
posterior      = estimate(burn_in, 20)
# compute the posterior draws of realized regime indicators
regimes        = compute_regime_probabilities(posterior)
# compute the posterior draws of filtered probabilities
filtered       = compute_regime_probabilities(posterior, "filtered")
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
  specify_bsvar_msh$new(p = 1, M = 2) |>
  estimate(S = 10) |> 
  estimate(S = 20) -> posterior
regimes        = compute_regime_probabilities(posterior)
filtered       = compute_regime_probabilities(posterior, "filtered")
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