Residuals of a Revision Model

Difference between the observed target release and the smoothed estimate of the model's latent signal. These are measurement residuals of the release the model treats as its target – the efficient release for a kk_model, the most mature release included in the estimation for a jvn_model – not one-step-ahead prediction errors.

Usage

# S3 method for class 'revision_model'
residuals(object, ...)

Arguments

object

A fitted model object inheriting from revision_model, such as a kk_model or a jvn_model.

...

Ignored.

Value

A tibble with columns time and residual.

See also

Other revision nowcasting: coef.revision_model(), fitted.revision_model(), jvn_nowcast(), kk_nowcast(), logLik.revision_model(), nobs.revision_model(), plot.revision_model(), predict.revision_model(), print.revision_model(), revision_model, states(), summary.revision_model(), vcov.revision_model()

Examples

df <- get_nth_release(
  tsbox::ts_span(
    tsbox::ts_pc(dplyr::filter(reviser::gdp, id == "US")),
    start = "1980-01-01"
  ),
  n = 0:1
)
df <- na.omit(dplyr::select(df, -c("id", "pub_date")))
fit <- kk_nowcast(df, e = 1, model = "KK", method = "MLE")
head(residuals(fit))
#> # A tibble: 6 × 2
#>   time           residual
#>   <date>            <dbl>
#> 1 1980-07-01 -0.000000883
#> 2 1980-10-01 -0.00000391 
#> 3 1981-01-01 -0.00000223 
#> 4 1981-04-01  0.00000123 
#> 5 1981-07-01 -0.00000161 
#> 6 1981-10-01  0.00000315