Residuals of a JVN model

Difference between the most mature release included in the estimation and the smoothed estimate of the latent true value. These are measurement residuals of that release, not one-step-ahead prediction errors.

Usage

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

Arguments

object

An object of class jvn_model.

...

Ignored.

Value

A tibble with columns time and residual.

See also

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

Examples

# \donttest{
gdp_growth <- dplyr::filter(
  tsbox::ts_pc(reviser::gdp),
  id == "EA",
  time >= min(pub_date),
  time <= as.Date("2020-01-01")
)
gdp_growth <- tidyr::drop_na(gdp_growth)
df <- get_nth_release(gdp_growth, n = 0:3)

fit <- jvn_nowcast(df = df, e = 4, ar_order = 2, include_noise = FALSE)
head(residuals(fit))
#> # A tibble: 6 × 2
#>   time         residual
#>   <date>          <dbl>
#> 1 2002-10-01  0.0000826
#> 2 2003-01-01  0.000321 
#> 3 2003-04-01 -0.00242  
#> 4 2003-07-01  0.000411 
#> 5 2003-10-01 -0.00158  
#> 6 2004-01-01  0.0000534
# }