Evaluates magnitude hypotheses for impulse responses or cumulative multipliers

Computes posterior probabilities and summaries for magnitude conditions on impulse responses or cumulative dynamic multipliers.

Usage

magnitude_audit(
  object,
  type = c("irf", "cdm"),
  variable,
  shock,
  horizon,
  relation = c("<", "<=", ">", ">=", "=="),
  value = 0,
  compare_to = NULL,
  absolute = FALSE,
  probability = 0.9,
  draws = FALSE,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

Arguments

object: A posterior model object or response object.
type: Posterior response quantity to evaluate.
variable: Deprecated. Use variables instead.
shock: Deprecated. Use shocks instead.
horizon: Horizon selection on the left-hand side.
relation: Comparison operator.
value: Scalar comparison value for threshold statements.
compare_to: Optional right-hand-side response specification with elements variable, shock, and horizon.
absolute: If TRUE, compare absolute responses.
probability: Probability mass of the equal-tailed credible interval for the posterior difference.
draws: If TRUE, return draw-level gaps and indicators.
model: Label identifying the model specification.
scale_by: Optional scaling mode for CDMs.
scale_var: Optional scaling variable specification.
...: Additional arguments passed to computation methods.

Value

A bsvar_post_tbl with posterior hypothesis summaries including posterior_prob, mean, median, lower, and upper columns.

Examples

data(us_fiscal_lsuw, package = "bsvars")
spec <- bsvars::specify_bsvar$new(us_fiscal_lsuw, p = 1)
#> The identification is set to the default option of lower-triangular structural matrix.
post <- bsvars::estimate(spec, S = 5, show_progress = FALSE)

mag <- magnitude_audit(post, variable = "gdp", shock = "gdp",
                       horizon = 0, relation = ">")
print(mag)
#> # A tibble: 1 × 27
#>   model  object_type variable shock horizon relation posterior_prob mean_gap
#>   <chr>  <chr>       <chr>    <chr>   <dbl> <chr>             <dbl>    <dbl>
#> 1 model1 irf         gdp      gdp         0 >                     1   0.0455
#> # ℹ 19 more variables: median_gap <dbl>, lower_gap <dbl>, upper_gap <dbl>,
#> #   analysis_type <chr>, interval_probability <dbl>, interval_method <chr>,
#> #   scale_by <chr>, scale_var <chr>, scale_factor <dbl>, units <chr>,
#> #   source_package <chr>, source_version <chr>, n_draws <int>,
#> #   rhs_variable <chr>, rhs_shock <chr>, rhs_horizon <dbl>, rhs_value <dbl>,
#> #   absolute <lgl>, audit_type <chr>