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Provides summary of the Savage-Dickey density ratios for verification of structural shocks homoskedasticity.

Usage

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

Arguments

object

an object of class SDDRvolatility obtained using the verify_volatility() function.

...

additional arguments affecting the summary produced.

Value

A table reporting the logarithm of Bayes factors of homoskedastic to heteroskedastic posterior odds "log(SDDR)" for each structural shock, their numerical standard errors "NSE", and the implied posterior probability of the homoskedasticity and heteroskedasticity hypothesis, "Pr[homoskedasticity|data]" and "Pr[heteroskedasticity|data]" respectively.

Author

Tomasz Woźniak wozniak.tom@pm.me

Examples

# upload data
data(us_fiscal_lsuw)

# specify the model and set seed
specification  = specify_bsvar_msh$new(us_fiscal_lsuw, p = 1, M = 2)
#> The identification is set to the default option of lower-triangular structural matrix.
set.seed(123)

# estimate the model
posterior      = estimate(specification, 10)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Gibbs sampler for the SVAR-stationaryMSH model             |
#> **************************************************|
#>  Progress of the MCMC simulation for 10 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|

# verify heteroskedasticity
sddr           = verify_volatility(posterior)
summary(sddr)
#>  **************************************************|
#>  bsvars: Bayesian Structural Vector Autoregressions|
#>  **************************************************|
#>    Summary of structural shocks                    |
#>       homoskedasticity verification                |
#>  **************************************************|
#>         log(SDDR) NSE Pr[homoskedasticity|data] Pr[heteroskedasticity|data]
#> shock 1  2.309722   0                0.90967904                  0.09032096
#> shock 2 -1.323233   0                0.21028091                  0.78971909
#> shock 3 -3.819532   0                0.02146712                  0.97853288

# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
  specify_bsvar_msh$new(p = 1, M = 2) |>
  estimate(S = 10) |> 
  verify_volatility() |> 
  summary() -> sddr_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 10 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|
#>  **************************************************|
#>  bsvars: Bayesian Structural Vector Autoregressions|
#>  **************************************************|
#>    Summary of structural shocks                    |
#>       homoskedasticity verification                |
#>  **************************************************|