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Provides posterior summary of regime probabilities

Source: R/summary.R
summary.PosteriorRegimePr.Rd

Provides posterior summary of regime probabilities including their mean, standard deviations, as well as 5 and 95 percentiles.

Usage

# S3 method for class 'PosteriorRegimePr'
summary(object, ...)

Arguments

object

an object of class PosteriorRegimePr obtained using the compute_regime_probabilities() function containing posterior draws of regime allocations.

...

additional arguments affecting the summary produced.

Value

A list reporting the posterior mean and standard deviations of the regime probabilities.

See also

compute_regime_probabilities

Author

Tomasz Woźniak wozniak.tom@pm.me

Examples

specification  = specify_bsvar_msh$new(us_fiscal_lsuw)
#> The identification is set to the default option of lower-triangular structural matrix.
burn_in        = estimate(specification, 5)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Gibbs sampler for the SVAR-stationaryMSH model             |
#> **************************************************|
#>  Progress of the MCMC simulation for 5 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|
posterior      = estimate(burn_in, 5)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Gibbs sampler for the SVAR-stationaryMSH model             |
#> **************************************************|
#>  Progress of the MCMC simulation for 5 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|

# compute regime probabilities
rp             = compute_regime_probabilities(posterior)
rp_summary     = summary(rp)
head(rp_summary$MarkovProcess1$regime1) # browse the results
#>   mean        sd
#> 1  0.0 0.0000000
#> 2  0.0 0.0000000
#> 3  0.2 0.4472136
#> 4  0.0 0.0000000
#> 5  0.0 0.0000000
#> 6  0.0 0.0000000

# workflow with the pipe |>
############################################################
us_fiscal_lsuw |>
  specify_bsvar_msh$new() |>
  estimate(S = 5) |> 
  estimate(S = 5) |> 
  compute_regime_probabilities() |>
  summary() -> rp_summary
#> The identification is set to the default option of lower-triangular structural matrix.
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Gibbs sampler for the SVAR-stationaryMSH model             |
#> **************************************************|
#>  Progress of the MCMC simulation for 5 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Gibbs sampler for the SVAR-stationaryMSH model             |
#> **************************************************|
#>  Progress of the MCMC simulation for 5 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|
head(rp_summary$MarkovProcess1$regime1) # browse the results
#>   mean        sd
#> 1  0.0 0.0000000
#> 2  0.0 0.0000000
#> 3  0.4 0.5477226
#> 4  0.0 0.0000000
#> 5  0.0 0.0000000
#> 6  0.0 0.0000000