Forecast Aggregate model

Usage

forecast_model(
  model,
  exog_predictions = NULL,
  n.ahead = 10,
  ci.levels = c(0.5, 0.66, 0.95),
  exog_fill_method = "AR",
  ar.fill.max = 4,
  plot.forecast = TRUE,
  uncertainty_sample = 100,
  quiet = FALSE
)

Arguments

model: An aggregate model object of class 'aggmod'.
exog_predictions: A data.frame or tibble with values for the exogenous values. The number of rows of this data must be equal to n.ahead.
n.ahead: Periods to forecast ahead
ci.levels: Numeric vector. Vector with confidence intervals to be calculated. Default: c(0.5,0.66,0.95)
exog_fill_method: Character, either 'AR' or 'last'. When no exogenous values have been provided, these must be inferred. When option 'exog_fill_method = "AR"' then an autoregressive model is used to further forecast the exogenous values. With 'last', simply the last available value is used.
ar.fill.max: Integer. When no exogenous values have been provided, these must be inferred. If option 'exog_fill_method = "AR"' then an autoregressive model is used to further forecast the exogenous values. This options determines the number of AR terms that should be used. Default is 4.
plot.forecast: Logical. Should the result be plotted? Default is TRUE.
uncertainty_sample: Integer. Number of draws to be made for the error bars. Default is 100.
quiet: Logical. Should messages about the forecast procedure be suppressed?

Value

An object of class aggmod.forecast

Examples

spec <- dplyr::tibble(
  type = c(
    "d",
    "d",
    "n"
  ),
  dependent = c(
    "StatDiscrep",
    "TOTS",
    "Import"
  ),
  independent = c(
    "TOTS - FinConsExpHH - FinConsExpGov - GCapitalForm - Export",
    "GValueAdd + Import",
    "FinConsExpHH + GCapitalForm"
  )
)

fa <- list(geo = "AT", s_adj = "SCA", unit = "CLV05_MEUR")
fb <- list(geo = "AT", s_adj = "SCA", unit = "CP_MEUR")
filter_list <- list("P7" = fa, "YA0" = fb, "P31_S14_S15" = fa,
"P5G" = fa, "B1G" = fa, "P3_S13" = fa, "P6" = fa)
if (FALSE) {
a <- run_model(specification = spec, dictionary = NULL,
inputdata_directory = NULL, filter_list = filter_list,
download = TRUE, save_to_disk = NULL, present = FALSE)
forecast(a)
}