Converts posterior forecasts to APRScenario format — as_apr_cond

Transforms draws or summaries from the posterior predictive distribution into the lower, centre, and upper paths used for conditional forecast analysis by APRScenario.

Usage

as_apr_cond_forc(object, ...)

# S3 method for class 'bsvar_post_tbl'
as_apr_cond_forc(
  object,
  center = c("median", "mean"),
  origin = NULL,
  frequency = c("quarter", "month", "year", "day"),
  ...
)

# S3 method for class 'Forecasts'
as_apr_cond_forc(
  object,
  probability = 0.9,
  center = c("median", "mean"),
  origin = NULL,
  frequency = c("quarter", "month", "year", "day"),
  model = "model1",
  ...
)

# S3 method for class 'PosteriorBSVAR'
as_apr_cond_forc(
  object,
  horizon = NULL,
  probability = 0.9,
  center = c("median", "mean"),
  origin = NULL,
  frequency = c("quarter", "month", "year", "day"),
  model = "model1",
  ...
)

Arguments

object: A posterior model object, a Forecasts object, or a posterior forecast table returned by tidy_forecast().
...: Additional arguments passed to tidy_forecast().
center: Posterior location summary represented by APRScenario's center column.
origin: Optional Date origin for turning forecast horizons into APR style hor dates.
frequency: Step size used with origin. One of "quarter", "month", "year", or "day".
probability: Probability mass of the equal-tailed credible interval.
model: Label identifying the model specification.
horizon: Forecast horizon when object is a posterior model object.

Value

A data frame with columns hor, variable, lower, center, and upper, suitable for use with conditional forecast analysis with APRScenario.

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)

apr_forc <- as_apr_cond_forc(post, horizon = 3)
head(apr_forc)
#>   hor variable      lower    center     upper  model
#> 1   1      ttr  -8.996956 -8.868245 -8.828637 model1
#> 2   2      ttr  -9.283976 -8.837319 -8.728452 model1
#> 3   3      ttr -10.456010 -8.788829 -8.731512 model1
#> 4   1       gs  -9.874547 -9.779793 -8.668058 model1
#> 5   2       gs  -9.968457 -9.723177 -8.843310 model1
#> 6   3       gs -10.269563 -9.857588 -9.284399 model1