Provides summary of verifying hypotheses about autoregressive parameters

Provides summary of the Savage-Dickey density ratios for verification of hypotheses about autoregressive parameters.

Usage

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

Arguments

object

an object of class SDDRautoregression obtained using the verify_autoregression() function.

...

additional arguments affecting the summary produced.

Value

A table reporting the logarithm of Bayes factors of the restriction against no restriction posterior odds in "log(SDDR)", its numerical standard error "NSE", and the implied posterior probability of the restriction holding or not hypothesis, "Pr[H0|data]" and "Pr[H1|data]" respectively.

See also

verify_autoregression

Author

Tomasz Woźniak wozniak.tom@pm.me

Examples

# upload data
data(us_fiscal_lsuw)

# specify the model and set seed
specification  = specify_bsvar_sv$new(us_fiscal_lsuw, p = 1)
#> 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-SV model              |
#>    Non-centred SV model is estimated              |
#> **************************************************|
#>  Progress of the MCMC simulation for 10 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|

# verify autoregression
H0             = matrix(NA, ncol(us_fiscal_lsuw), ncol(us_fiscal_lsuw) + 1)
H0[1,3]        = 0        # a hypothesis of no Granger causality from gdp to ttr
sddr           = verify_autoregression(posterior, H0)
summary(sddr)
#>  **************************************************|
#>  bsvars: Bayesian Structural Vector Autoregressions|
#>  **************************************************|
#>    Summary of hypothesis verification              |
#>       for autoregressive parameters                |
#>  **************************************************|
#>  log(SDDR) NSE Pr[H0|data] Pr[H1|data]
#>  -81.18968   0  5.4925e-36           1

# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
  specify_bsvar_sv$new(p = 1) |>
  estimate(S = 10) |> 
  verify_autoregression(hypothesis = H0) |> 
  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-SV model              |
#>    Non-centred SV model is estimated              |
#> **************************************************|
#>  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 hypothesis verification              |
#>       for autoregressive parameters                |
#>  **************************************************|