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Time-to-threshold summaries for posterior IRFs and CDMs

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

Summarise the first horizon at which a response satisfies a threshold condition.

Usage

time_to_threshold(object, ...)

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

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

# S3 method for class 'PosteriorBSVAR'
time_to_threshold(
  object,
  horizon = NULL,
  type = c("irf", "cdm"),
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  relation = c(">", ">=", "<", "<="),
  value = 0,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

# S3 method for class 'PosteriorBSVARMIX'
time_to_threshold(
  object,
  horizon = NULL,
  type = c("irf", "cdm"),
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  relation = c(">", ">=", "<", "<="),
  value = 0,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

# S3 method for class 'PosteriorBSVARMSH'
time_to_threshold(
  object,
  horizon = NULL,
  type = c("irf", "cdm"),
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  relation = c(">", ">=", "<", "<="),
  value = 0,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

# S3 method for class 'PosteriorBSVARSV'
time_to_threshold(
  object,
  horizon = NULL,
  type = c("irf", "cdm"),
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  relation = c(">", ">=", "<", "<="),
  value = 0,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

# S3 method for class 'PosteriorBSVART'
time_to_threshold(
  object,
  horizon = NULL,
  type = c("irf", "cdm"),
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  relation = c(">", ">=", "<", "<="),
  value = 0,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

# S3 method for class 'PosteriorBSVARSIGN'
time_to_threshold(
  object,
  horizon = NULL,
  type = c("irf", "cdm"),
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  relation = c(">", ">=", "<", "<="),
  value = 0,
  absolute = FALSE,
  probability = 0.9,
  model = "model1",
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

# S3 method for class 'PosteriorCDM'
time_to_threshold(
  object,
  variables = NULL,
  shocks = NULL,
  variable = NULL,
  shock = NULL,
  relation = c(">", ">=", "<", "<="),
  value = 0,
  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.

relation

Comparison operator.

value

Threshold value.

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, relation, threshold, mean_horizon, median_horizon, sd_horizon, lower_horizon, upper_horizon, and reached_prob.

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)

ttt <- time_to_threshold(post, horizon = 3, relation = ">", value = 0)
print(ttt)
#> # A tibble: 9 × 12
#>   model  object_type           variable shock relation threshold mean_horizon
#>   <chr>  <chr>                 <chr>    <chr> <chr>        <dbl>        <dbl>
#> 1 model1 time_to_threshold_irf ttr      ttr   >                0            0
#> 2 model1 time_to_threshold_irf ttr      gs    >                0            1
#> 3 model1 time_to_threshold_irf ttr      gdp   >                0            1
#> 4 model1 time_to_threshold_irf gs       ttr   >                0            1
#> 5 model1 time_to_threshold_irf gs       gs    >                0            0
#> 6 model1 time_to_threshold_irf gs       gdp   >                0            1
#> 7 model1 time_to_threshold_irf gdp      ttr   >                0            0
#> 8 model1 time_to_threshold_irf gdp      gs    >                0           NA
#> 9 model1 time_to_threshold_irf gdp      gdp   >                0            0
#> # ℹ 5 more variables: median_horizon <dbl>, sd_horizon <dbl>,
#> #   lower_horizon <dbl>, upper_horizon <dbl>, reached_prob <dbl>