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Peak response summaries for posterior IRFs and CDMs

Source: R/response-summary.R
peak_response.Rd

Summarise the peak response level and the horizon at which that peak occurs.

Usage

peak_response(object, ...)

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

# S3 method for class 'PosteriorIR'
peak_response(
  object,
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  ...
)

# S3 method for class 'PosteriorBSVAR'
peak_response(
  object,
  horizon = NULL,
  type = c("irf", "cdm"),
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

# S3 method for class 'PosteriorBSVARMIX'
peak_response(
  object,
  horizon = NULL,
  type = c("irf", "cdm"),
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

# S3 method for class 'PosteriorBSVARMSH'
peak_response(
  object,
  horizon = NULL,
  type = c("irf", "cdm"),
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

# S3 method for class 'PosteriorBSVARSV'
peak_response(
  object,
  horizon = NULL,
  type = c("irf", "cdm"),
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

# S3 method for class 'PosteriorBSVART'
peak_response(
  object,
  horizon = NULL,
  type = c("irf", "cdm"),
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

# S3 method for class 'PosteriorBSVARSIGN'
peak_response(
  object,
  horizon = NULL,
  type = c("irf", "cdm"),
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

# S3 method for class 'PosteriorCDM'
peak_response(
  object,
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  ...
)

Arguments

object

A posterior model object, PosteriorIR, or PosteriorCDM.

...

Additional arguments passed to computation methods.

variables

Optional response-variable subset (character or integer vector).

shocks

Optional shock subset (character or integer vector).

variable

Deprecated. Use variables instead.

shock

Deprecated. Use shocks instead.

absolute

If TRUE, search for the largest absolute response.

probability

Equal-tailed interval probability.

model

Optional model identifier.

horizon

Maximum horizon used when object is a posterior model object.

type

Response type for posterior model objects: "irf" or "cdm".

scale_by

Optional scaling mode for CDMs.

scale_var

Optional scaling variable specification.

Value

A bsvar_post_tbl with columns model, object_type, variable, shock, mean_value, median_value, sd_value, lower_value, upper_value, mean_horizon, median_horizon, sd_horizon, lower_horizon, and upper_horizon.

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)

pk <- peak_response(post, horizon = 3)
print(pk)
#> # A tibble: 9 × 14
#>   model  object_type variable shock mean_value median_value sd_value lower_value
#>   <chr>  <chr>       <chr>    <chr>      <dbl>        <dbl>    <dbl>       <dbl>
#> 1 model1 peak_irf    ttr      ttr     0.0515       0.0312    0.0331      0.0296 
#> 2 model1 peak_irf    ttr      gs      0.00735      0.00412   0.00599     0.00275
#> 3 model1 peak_irf    ttr      gdp     0.0328       0.000101  0.0526      0      
#> 4 model1 peak_irf    gs       ttr     0.000155     0.00226   0.00720    -0.00957
#> 5 model1 peak_irf    gs       gs      0.0544       0.0286    0.0570      0.0269 
#> 6 model1 peak_irf    gs       gdp     0.0387       0         0.0864      0      
#> 7 model1 peak_irf    gdp      ttr     0.00291      0.0119    0.0794     -0.0967 
#> 8 model1 peak_irf    gdp      gs      0.0307       0.0122    0.0392      0.00107
#> 9 model1 peak_irf    gdp      gdp     0.0990       0.0372    0.116       0.0122 
#> # ℹ 6 more variables: upper_value <dbl>, mean_horizon <dbl>,
#> #   median_horizon <dbl>, sd_horizon <dbl>, lower_horizon <dbl>,
#> #   upper_horizon <dbl>