Verifies hypotheses involving autoregressive parameters

Computes the logarithm of Bayes factor for the joint hypothesis, \(H_0\), possibly for many autoregressive parameters represented by argument hypothesis via Savage-Dickey Density Ration (SDDR). The logarithm of Bayes factor for this hypothesis can be computed using the SDDR as the difference of logarithms of the marginal posterior distribution ordinate at the restriction less the marginal prior distribution ordinate at the same point: $$log p(H_0 | data) - log p(H_0)$$ Therefore, a negative value of the difference is the evidence against hypothesis. The estimation of both elements of the difference requires numerical integration.

Usage

# S3 method for class 'PosteriorBSVARSV'
verify_autoregression(posterior, hypothesis)

Arguments

posterior

the posterior element of the list from the estimation outcome

hypothesis

an NxK matrix of the same dimension as the autoregressive matrix \(A\) with numeric values for the parameters to be verified, in which case the values represent the joint hypothesis, and missing value NA for these parameters that are not tested

Value

An object of class SDDRautoregression that is a list of three components:

logSDDR a scalar with values of the logarithm of the Bayes factors for the autoregressive hypothesis for each of the shocks

log_SDDR_se an N-vector with estimation standard errors of the logarithm of the Bayes factors reported in output element logSDDR that are computed based on 30 random sub-samples of the log-ordinates of the marginal posterior and prior distributions.

components a list of three components for the computation of the Bayes factor

log_denominator

an N-vector with values of the logarithm of the Bayes factor denominators

log_numerator

an N-vector with values of the logarithm of the Bayes factor numerators

log_numerator_s

an NxS matrix of the log-full conditional posterior density ordinates computed to estimate the numerator

log_denominator_s

an NxS matrix of the log-full conditional posterior density ordinates computed to estimate the denominator

se_components

a 30-vector containing the log-Bayes factors on the basis of which the standard errors are computed

References

Woźniak, T., and Droumaguet, M., (2024) Bayesian Assessment of Identifying Restrictions for Heteroskedastic Structural VARs

Author

Tomasz Woźniak wozniak.tom@pm.me

Examples

# simple workflow
############################################################
# 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)

# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
  specify_bsvar_sv$new(p = 1) |>
  estimate(S = 10) |> 
  verify_autoregression(hypothesis = H0) -> sddr
#> 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
#> **************************************************|