news: News Decomposition

In dfms: Dynamic Factor Models

News Decomposition

Description

Compute the Banbura and Modugno (2014) news decomposition of forecast updates. Given an old vintage and an updated vintage, the function decomposes the forecast revision at t.fcst into contributions from new releases.

Usage

news(object, ...)

## S3 method for class 'dfm'
news(
  object,
  comparison,
  t.fcst = nrow(object$X_imp),
  target.vars = NULL,
  series = NULL,
  standardized = FALSE,
  ...
)

## S3 method for class 'dfm_news'
print(x, digits = 4L, ...)

## S3 method for class 'dfm_news_list'
print(x, digits = 4L, ...)

## S3 method for class 'dfm_news_list'
x$name

## S3 method for class 'dfm_news_list'
x[[i]]

## S3 method for class 'dfm_news_list'
x[i]

## S3 method for class 'dfm_news_list'
as.data.frame(x, ...)

Arguments

object	a dfm object for the old vintage.
...	not used.
comparison	a dfm object or a new dataset for the updated vintage.
t.fcst	integer. Forecast target time index.
target.vars	Integer or character identifying target variables. Defaults to all variables.
series	optional character vector for naming variables.
standardized	logical. Return results on standardized scale?
x	an object of class 'dfm_news' or 'dfm_news_list'.
digits	integer. Number of digits to print.
name	character. Element name.
i	index. Element position or name.

Details

Let y_t^{old} and y_t^{new} be the old and new forecasts of a target series at t = t_{fcst}. For each new release i (a previously missing observation that becomes observed), the innovation is

\nu_i = x_i^{new} - \hat{x}_i^{old},

where \hat{x}_i^{old} is the smoothed estimate from the old vintage. The revision is decomposed as

y_t^{new} - y_t^{old} = \sum_i g_i \nu_i,

with gain weights computed from Kalman smoother covariances:

g = \sigma_y C_y P_1 P_2^{-1}.

Here \sigma_y is the target series standard deviation, C_y is the loading row for the target series, P_1 collects cross-covariances between the target and each news item, and P_2 is the covariance matrix of the news items (including measurement error where appropriate). See Section 2.3 and Appendix D in Banbura and Modugno (2014).

The function uses the system matrices and scaling from the new vintage. The old data are re-standardized to the new-vintage scale before smoothing so that innovations and gains are computed on a consistent scale. Set standardized = FALSE to report results on the original data scale.

Value

For a single target, a dfm_news object with elements:

• y_old: old forecast for the target variable at t.fcst.

• y_new: new forecast for the target variable at t.fcst.

• news_df: data frame with one row per series and columns:

  • series: series name.

  • actual: actual release (if any).

  • forecast: old-vintage forecast of the release.

  • news: total innovation for the series on the output scale. If there is a single release, news equals actual - forecast. With multiple releases, news aggregates those innovations for the series.

  • gain: effective weight on news such that impact = news * gain (on the output scale).

  • gain_std: effective weight on the standardized innovations.

  • impact: contribution of the series to the target revision.

If target.vars selects multiple targets, a dfm_news_list object is returned, where each element is a dfm_news object and list names correspond to targets.

Note

This implementation is translated from the original MATLAB codes and is consistent with the BM2014 news decomposition formulas. If the model was estimated with max.missing < 1 and na.rm.method = "LE" in tsnarmimp (called by DFM()), leading or trailing rows with many missing values may be removed by DFM(). If old and new vintages are both dfm objects, and they drop different rows, then t.fcst can become out of bounds. When comparison is provided as raw data, news() drops object$rm.rows from the new dataset (if present) and forces max.missing = 1 for the re-estimation call to keep row alignment. To avoid issues, estimate both vintages with max.missing = 1. For mixed-frequency or idiosyncratic AR(1) models, news() relies on the full state-space matrices stored in dfm$ss_full.

References

Banbura, M., & Modugno, M. (2014). Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. Journal of Applied Econometrics, 29(1), 133-160.

See Also

dfms-package

Examples

# (1) Monthly DFM example
X <- collapse::qM(BM14_M)[, BM14_Models$medium[BM14_Models$freq == "M"]]
X_old <- X
# Creating earlier vintage
X_old[nrow(X) - 1, sample(which(is.finite(X[nrow(X) - 1, ]) & is.na(X[nrow(X), ])), 5)] <- NA
X_old[nrow(X), sample(which(is.finite(X[nrow(X), ])), 5)] <- NA
# Estimating DFM
dfm <- DFM(X_old, r = 2, p = 2, em.method = "none")
# News computation (second DFM fit internally with same settings and rows)
res <- news(dfm, X, target.vars = c("ip_tot_cstr", "orders", "urx"))
# See results
print(res)
head(res$news_df)

# (2) MQ nowcast of GDP (idio.ar1 = FALSE for speed)
library(magrittr)
library(xts)
# Creating MQ dataset
BM14 <- merge(BM14_M, BM14_Q)
BM14[, BM14_Models$log_trans] %<>% log()
BM14[, BM14_Models$freq == "M"] %<>% diff()
BM14[, BM14_Models$freq == "Q"] %<>% diff(3)
X <- BM14[-1, BM14_Models$small]
quarterly.vars <- BM14_Models$series[BM14_Models$small & BM14_Models$freq == "Q"]
# Creating earlier vintage
X_old <- X
X_old[355, c("ip_tot_cstr", "new_cars")] <- NA
X_old[356, c("new_cars", "pms_pmi", "euro325", "capacity")] <- NA
# Estimating DFM
dfm <- DFM(X_old, r = 2, p = 2, quarterly.vars = quarterly.vars, max.missing = 1)
# News computation (second DFM fit internally with same settings and rows)
res_mq <- news(dfm, X, t.fcst = 356, target.vars = "gdp")
# See results
print(res_mq)
head(res_mq$news_df)

dfms documentation built on June 19, 2026, 1:06 a.m.