Skip to contents

Posterior probability statements for impulse responses

Source: R/hypothesis.R
hypothesis_irf.Rd

Evaluate threshold or pairwise posterior probability statements on impulse response draws.

Usage

hypothesis_irf(object, ...)

# Default S3 method
hypothesis_irf(object, ...)

# S3 method for class 'PosteriorIR'
hypothesis_irf(
  object,
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  horizon,
  relation = c("<", "<=", ">", ">=", "=="),
  value = 0,
  compare_to = NULL,
  absolute = FALSE,
  probability = 0.9,
  draws = FALSE,
  model = "model1",
  ...
)

# S3 method for class 'PosteriorBSVAR'
hypothesis_irf(
  object,
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  horizon,
  relation = c("<", "<=", ">", ">=", "=="),
  value = 0,
  compare_to = NULL,
  absolute = FALSE,
  probability = 0.9,
  draws = FALSE,
  model = "model1",
  ...
)

Arguments

object

A posterior model object or a PosteriorIR object.

...

Additional arguments passed to computation methods.

variables

Response variable selection on the left-hand side (character or integer vector).

shocks

Shock selection on the left-hand side (character or integer vector).

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

Equal-tailed interval probability used for gap summaries.

draws

If TRUE, return draw-level gaps and indicators.

model

Optional model identifier.

Value

A bsvar_post_tbl with columns model, object_type, variable, shock, horizon, relation, posterior_prob, mean_gap, median_gap, lower_gap, and upper_gap. When draws = TRUE, columns draw, gap, and satisfied replace the summary statistics. Additional columns rhs_variable, rhs_shock, rhs_horizon, rhs_value, and absolute describe the right-hand side.

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)

h <- hypothesis_irf(post, variable = "gdp", shock = "gdp",
                    horizon = 0:2, relation = ">", value = 0)
#> Warning: In hypothesis_irf(): 'variable' is deprecated and will be removed in a future version.
#> Use 'variables' instead.
#> Warning: In hypothesis_irf(): 'shock' is deprecated and will be removed in a future version.
#> Use 'shocks' instead.
print(h)
#> # A tibble: 3 × 16
#>   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   0.0446
#> 2 model1 irf         gdp      gdp         1 >                     1   0.0757
#> 3 model1 irf         gdp      gdp         2 >                     1   0.160 
#> # ℹ 8 more variables: median_gap <dbl>, lower_gap <dbl>, upper_gap <dbl>,
#> #   rhs_variable <chr>, rhs_shock <chr>, rhs_horizon <dbl>, rhs_value <dbl>,
#> #   absolute <lgl>