Ragged-Edge Nowcasting with bridgr • bridgr

Why Ragged Edges Matter

Mixed-frequency nowcasting usually happens at the ragged edge: the lower-frequency target has not been released yet, and the higher-frequency indicators are only partially observed for the current target period.

bridgr handles that situation through indic_predict, which controls how the missing high-frequency tail is completed before the target equation is estimated or forecast.

The currently supported options are:

"last": extend the latest available high-frequency observation.
"mean": extend the mean of the latest available high-frequency block.
"auto.arima": fit an automatic ARIMA model to each indicator and forecast the missing observations.
"ets": fit an ETS model to each indicator and forecast the missing observations.
"direct": do not forecast the indicator at all. Instead, align the latest observed complete high-frequency blocks directly to the target periods.

A Ragged-Edge Example

We start from the package’s quarterly GDP growth series and monthly barometer, then remove the last quarterly GDP observation and the last two monthly indicator observations. That creates a clean nowcast setup: the final quarter is now forecasted rather than estimated, and the monthly indicator is only partially observed for that forecast quarter.

gdp_growth <- suppressMessages(tsbox::ts_na_omit(tsbox::ts_pc(gdp)))
gdp_nowcast <- gdp_growth |>
  dplyr::slice_head(n = nrow(gdp_growth) - 1)

baro_ragged <- baro |>
  dplyr::slice_head(n = nrow(baro) - 2)

tail(gdp_growth)
#> # A tibble: 6 × 2
#>   time       values
#>   <date>      <dbl>
#> 1 2021-07-01  2.34 
#> 2 2021-10-01  0.411
#> 3 2022-01-01  0.105
#> 4 2022-04-01  1.03 
#> 5 2022-07-01  0.255
#> 6 2022-10-01  0.102
tail(gdp_nowcast)
#> # A tibble: 6 × 2
#>   time       values
#>   <date>      <dbl>
#> 1 2021-04-01  2.47 
#> 2 2021-07-01  2.34 
#> 3 2021-10-01  0.411
#> 4 2022-01-01  0.105
#> 5 2022-04-01  1.03 
#> 6 2022-07-01  0.255
tail(baro_ragged)
#> # A tibble: 6 × 2
#>   time       values
#>   <date>      <dbl>
#> 1 2022-05-01   90.9
#> 2 2022-06-01   95.7
#> 3 2022-07-01   91.7
#> 4 2022-08-01   89.4
#> 5 2022-09-01   89.0
#> 6 2022-10-01   87.5

The target history now ends one quarter earlier, while the monthly indicator contains only a partial block for the quarter we want to nowcast.

Comparing Indicator Completion Rules

predict_methods <- c("last", "mean", "auto.arima", "ets")

models <- lapply(
  predict_methods,
  function(method) {
    bridge(
      target = gdp_nowcast,
      indic = baro_ragged,
      indic_predict = method,
      indic_aggregators = "mean",
      target_lags = 1,
      h = 1
    )
  }
)
names(models) <- predict_methods

dplyr::bind_rows(
  lapply(
    predict_methods,
    function(method) {
      fc <- forecast(models[[method]])
      dplyr::tibble(
        method = method,
        forecast_time = fc$time,
        forecast = as.numeric(fc$mean)
      )
    }
  )
)
#> # A tibble: 4 × 3
#>   method     forecast_time forecast
#>   <chr>      <date>           <dbl>
#> 1 last       2022-10-01      -0.885
#> 2 mean       2022-10-01      -0.810
#> 3 auto.arima 2022-10-01      -0.670
#> 4 ets        2022-10-01      -0.884

The model specification is the same in each case. The only difference is how the missing monthly observations are completed before the indicator is aggregated to the quarterly frequency.

Looking at the Forecast Regressor Set

The easiest way to see what each method is doing is to inspect forecast_set, which stores the target-period regressors used for the final forecast.

lapply(
  models[c("last", "mean")],
  function(model) {
    model$forecast_set
  }
)
#> $last
#> # A tibble: 1 × 2
#>   time       baro_ragged
#>   <date>           <dbl>
#> 1 2022-10-01        87.5
#> 
#> $mean
#> # A tibble: 1 × 2
#>   time       baro_ragged
#>   <date>           <dbl>
#> 1 2022-10-01        88.2

With "last", the last observed monthly value is extended forward. With "mean", the mean of the latest available high-frequency block is extended forward instead.

Direct Alignment

"direct" takes a different route. The indicator is not forecasted. Instead, bridgr assigns the latest complete high-frequency blocks backward to the target periods. That is closest to a direct MIDAS-style setup.

direct_model <- bridge(
  target = gdp_nowcast,
  indic = baro_ragged,
  indic_predict = "direct",
  indic_aggregators = "unrestricted",
  h = 1
)

forecast(direct_model)
#> Bridge forecast
#> -----------------------------------
#> Target series: gdp_nowcast
#> Forecast horizon: 1
#> Uncertainty: point forecast only
#> -----------------------------------
#>   time       mean  
#> 1 2022-10-01 -0.150

This is especially useful when you want to avoid a separate indicator forecasting step and prefer to work only with observed high-frequency data.

When to Use Which Option

As a rough guide:

Use "last" when a simple carry-forward rule is acceptable.
Use "mean" when you want a stable deterministic fill based on the latest high-frequency block.
Use "auto.arima" or "ets" when the indicator has meaningful time-series structure and a separate extrapolation step is sensible.
Use "direct" when you want direct MIDAS-style alignment without indicator forecasting.

The good part is that the downstream workflow does not change. The same summary() and forecast() interface works across all of these nowcasting choices.