Get Dynamic Factor Model Forecasts

Provides access to forecasts and their associated standard deviations for both the original and transformed series. Optional argument allows to include the missing values estimate in the output.

Usage

get_forecasts(
  dfm_estimates,
  n_fcst = 3,
  estim_missing = FALSE,
  mask_q_m = FALSE
)

Arguments

dfm_estimates

An object of class "JD3_DFMESTIMATES", typically generated using the estimate_ml(), estimate_em(), or estimate_pca() function.

n_fcst

Integer. Number of forecast periods to consider. The default is 3.

estim_missing

Boolean. Indicates whether missing values should be estimated prior to the start of the forecasting period. The default is FALSE.

mask_q_m

Boolean. Indicates whether estimates for the first two months of each quarter should be masked when the factor type is Q. The default is FALSE.

Value

An object of class "JD3_DFMFORECASTS" is returned. The following are returned invisibly as a list:

• transformed_forecasts [[1]] the transformed series together with their forecasts;

• transformed_forecasts_stdev [[2]] standard deviations of the transformed series and their forecasts;

• forecasts [[3]] the original series together with their forecasts;

• forecasts_stdev [[4]] standard deviations of the original series and their forecasts;

• forecasts_only [[5]] the forecasts of the original series;

• forecasts_only_stdev [[6]] standard deviations of the forecasts of the original series.

See also

get_results() to obtain estimation results.

For more information, see the vignette:

utils::browseVignettes(), e.g. browseVignettes(package = "rjd3nowcasting")

Examples

if (FALSE) { # rjd3toolkit::get_java_version() >= rjd3toolkit::minimal_java_version
set.seed(100)
data <- ts(matrix(rnorm(500), 100, 5),
           frequency = 12,
           start = c(2010, 1))
data[100, 1] <- data[99:100, 2] <- data[(1:100)[-seq(3, 100, 3)], 5] <- NA

dfm <- create_model(
    nfactors = 2,
    nlags = 2,
    factors_type = c("M", "M", "YoY", "M", "Q"),
    factors_loading = matrix(data = TRUE, 5, 2),
    var_init = "Unconditional"
)

est_em <- estimate_em(dfm, data)

fcsts_em <- get_forecasts(est_em, n_fcst = 2)
}