Calculate latent VAR forecast error variance decompositions

Compute forecast error variance decompositions from mvgam models with Vector Autoregressive dynamics

Usage

fevd(object, ...)

# S3 method for mvgam
fevd(object, h = 10, ...)

Arguments

object

list object of class mvgam resulting from a call to mvgam() that used a Vector Autoregressive latent process model (either as VAR(cor = FALSE) or VAR(cor = TRUE); see VAR() for details)

...

ignored

h

Positive integer specifying the forecast horizon over which to calculate the IRF

Value

See mvgam_fevd-class for a full description of the quantities that are computed and returned by this function, along with key references.

See also

VAR(), irf(), stability(), mvgam_fevd-class

Author

Nicholas J Clark

Examples

# \donttest{
# Simulate some time series that follow a latent VAR(1) process
simdat <- sim_mvgam(
  family = gaussian(),
  n_series = 4,
  trend_model = VAR(cor = TRUE),
  prop_trend = 1
)
plot_mvgam_series(data = simdat$data_train, series = "all")


# Fit a model that uses a latent VAR(1)
mod <- mvgam(
  formula = y ~ -1,
  trend_formula = ~ 1,
  trend_model = VAR(cor = TRUE),
  family = gaussian(),
  data = simdat$data_train,
  chains = 2,
  silent = 2
)

# Plot the autoregressive coefficient distributions;
# use 'dir = "v"' to arrange the order of facets
# correctly
mcmc_plot(
  mod,
  variable = 'A',
  regex = TRUE,
  type = 'hist',
  facet_args = list(dir = 'v')
)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


# Calulate forecast error variance decompositions for each series
fevds <- fevd(mod, h = 12)

# Plot median contributions to forecast error variance
plot(fevds)


# View a summary of the error variance decompositions
summary(fevds)
#> # A tibble: 192 × 5
#>    shock                  horizon fevdQ50 fevdQ2.5 fevdQ97.5
#>    <chr>                    <int>   <dbl>    <dbl>     <dbl>
#>  1 Process_1 -> Process_1       1   1        1         1    
#>  2 Process_1 -> Process_1       2   0.861    0.500     0.986
#>  3 Process_1 -> Process_1       3   0.763    0.376     0.969
#>  4 Process_1 -> Process_1       4   0.708    0.313     0.957
#>  5 Process_1 -> Process_1       5   0.686    0.279     0.950
#>  6 Process_1 -> Process_1       6   0.677    0.271     0.946
#>  7 Process_1 -> Process_1       7   0.672    0.271     0.944
#>  8 Process_1 -> Process_1       8   0.669    0.273     0.942
#>  9 Process_1 -> Process_1       9   0.668    0.269     0.942
#> 10 Process_1 -> Process_1      10   0.668    0.268     0.941
#> # ℹ 182 more rows
# }