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Perform News Analysis for Forecast Updates in Dynamic Factor Models

Source: R/news.R
get_news.Rd

Performs a news analysis to quantify the impact of newly released data on forecast revisions between two consecutive dataset updates. The function isolates the contribution of new observations by comparing them to their forecasts obtained from the revised dataset (including past data revisions) and provides a detailed decomposition of the resulting forecast changes.

Usage

get_news(dfm_estimates, new_data, target_series = NULL, n_fcst = 3)

Arguments

dfm_estimates

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

new_data

A "mts" object containing the updated dataset.

target_series

Character. Name of the target series of interest. By default, the first series is used.

n_fcst

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

Value

An object of class "JD3_DFMNEWS" is returned. The following are returned invisibly as a list:

  • target_series [[1]] the value of the target_series argument;

  • weights_T [[2]] the weights of the news for the transformed series;

  • impacts_T [[3]] the impacts of the news (weights x news) for the transformed series;

  • forecasts_T [[4]] the old, revised (including past data revisions) and new forecasts of the transformed series;

  • weights [[5]] the weights of the news for the original series;

  • impacts [[6]] the impacts of the news (weights x news) for the original series;

  • forecasts [[7]] the old, revised (including past data revisions) and new forecasts of the original series.

References

Banbura, M., & Modugno, M. (2010). Maximum likelihood estimation of factor models on data sets with arbitrary patterns of missing data.

See also

create_model() to define a new Dynamic Factor model,

estimate_pca() for estimation using principal components analysis,

estimate_em() for estimation using EM algorithm,

estimate_ml() for estimation using maximum likelihood,

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_t1 <- ts(matrix(rnorm(500), 100, 5),
              frequency = 12,
              start = c(2010, 1))
data_t1[100, 1] <- data_t1[99:100, 2] <- data_t1[(1:100)[-seq(3, 100, 3)], 5] <- NA
data_t2 <- ts(rbind(data_t1, rep(NA, 5)),
              frequency = 12,
              start = c(2010, 1))
data_t2[100, 1] <- data_t2[99, 2] <- data_t2[101, 3] <- data_t2[101, 4] <- 1

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

est_em_a <- estimate_em(dfm_model, data_t1)

news_a <- get_news(est_em_a, data_t2, target_series = "Series 2", n_fcst = 2)
}