summary.SDDRautoregression: Provides summary of verifying hypotheses about autoregressive...

In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

Provides summary of verifying hypotheses about autoregressive parameters

Description

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.

Author(s)

Tomasz Woźniak [e-mail]

See Also

verify_autoregression

Examples

# upload data
data(us_fiscal_lsuw)

# specify the model and set seed
specification  = specify_bsvar_sv$new(us_fiscal_lsuw, p = 1)
set.seed(123)

# estimate the model
posterior      = estimate(specification, 10)

# 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)

# 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

bsvars documentation built on Oct. 24, 2024, 5:11 p.m.