Overview
bridgr is a mixed-frequency forecasting package for
bridge models, MIDAS-style regressions, and intermediate specifications
that estimate within-period weights from the data.
The core workflow is always the same:
- Provide one lower-frequency target series and one or more higher-frequency indicators.
- Decide how missing indicator observations should be handled with
indic_predict. - Decide how indicators should be aligned to the target frequency with
indic_aggregators. - Fit the target equation with
mf_model(), inspect it withsummary(), and produce target-period forecasts withforecast().
This vignette walks through that workflow with the package’s built-in Swiss GDP and barometer data.
Example Data
gdp_growth <- suppressMessages(tsbox::ts_na_omit(tsbox::ts_pc(gdp)))
head(gdp_growth)
#> # A tibble: 6 × 2
#> time values
#> <date> <dbl>
#> 1 2004-04-01 0.839
#> 2 2004-07-01 -0.104
#> 3 2004-10-01 0.242
#> 4 2005-01-01 0.860
#> 5 2005-04-01 1.06
#> 6 2005-07-01 1.15
head(baro)
#> # A tibble: 6 × 2
#> time values
#> <date> <dbl>
#> 1 2004-01-01 109.
#> 2 2004-02-01 108.
#> 3 2004-03-01 109.
#> 4 2004-04-01 110.
#> 5 2004-05-01 109.
#> 6 2004-06-01 105.gdp_growth is quarterly, while baro is
monthly. mf_model() recognizes the frequency mismatch
automatically and aligns the indicator to the target frequency before
fitting the target equation.
A Basic Bridge Model
bridge_model <- mf_model(
target = gdp_growth,
indic = baro,
indic_predict = "auto.arima",
indic_aggregators = "mean",
indic_lags = 1,
target_lags = 1,
h = 2
)
forecast(bridge_model)
#> Mixed-frequency forecast
#> -----------------------------------
#> Target series: gdp_growth
#> Forecast horizon: 2
#> Uncertainty: point forecast only
#> -----------------------------------
#> time mean
#> 1 2023-01-01 0.875
#> 2 2023-04-01 0.678The default mean aggregator is the classic bridge-model setup: each monthly block is completed first, then averaged to the quarterly frequency before the target equation is estimated.
The fitted object stores the aligned data that went into estimation and the future target-period regressor path used for forecasting.
tail(bridge_model$estimation_set)
#> # A tibble: 6 × 5
#> time gdp_growth baro baro_lag1 gdp_growth_lag1
#> <date> <dbl> <dbl> <dbl> <dbl>
#> 1 2021-07-01 2.34 112. 125. 2.47
#> 2 2021-10-01 0.411 104. 112. 2.34
#> 3 2022-01-01 0.105 97.4 104. 0.411
#> 4 2022-04-01 1.03 94.3 97.4 0.105
#> 5 2022-07-01 0.255 90.0 94.3 1.03
#> 6 2022-10-01 0.102 90.7 90.0 0.255
bridge_model$forecast_set
#> # A tibble: 2 × 4
#> time baro baro_lag1 gdp_growth_lag1
#> <date> <dbl> <dbl> <list>
#> 1 2023-01-01 97.4 90.7 <dbl [1]>
#> 2 2023-04-01 99.8 97.4 <dbl [1]>Standardized Output
summary() and forecast() use a stable
package-specific layout. The base output is the same across bridge,
mixed-frequency, and direct-alignment specifications. Additional
details, such as optimization summaries or uncertainty settings, are
appended only when they are relevant.
summary(bridge_model)
#> Mixed-frequency model summary
#> -----------------------------------
#> Target series: gdp_growth
#> Target frequency: quarter
#> Forecast horizon: 2
#> Estimation rows: 73
#> Regressors: baro, baro_lag1, gdp_growth_lag1
#> -----------------------------------
#> Target equation coefficients:
#> Estimate
#> (Intercept) -6.249
#> baro 0.151
#> baro_lag1 -0.084
#> gdp_growth_lag1 0.012
#> -----------------------------------
#> Model fit:
#> Statistic Value
#> R-squared 0.682
#> Adjusted R-squared 0.668
#> Residual standard error 0.773
#> -----------------------------------
#> Indicator summary:
#> Frequency Predict Aggregation
#> baro month auto.arima mean
#> -----------------------------------Forecast Visualization
The package also provides a built-in plotting method for fitted
bridge models. With type = "forecast", it shows the
observed target history together with the forecast path generated by the
model.
plot(bridge_model, type = "forecast")
Direct Alignment
If you set indic_predict = "direct", bridgr
switches from indicator forecasting to direct alignment based only on
observed complete high-frequency blocks. In that case, the latest
complete blocks are assigned backward to target periods instead of being
forecast forward first, and they are averaged within each target
period.
direct_model <- mf_model(
target = gdp_growth,
indic = baro,
indic_predict = "direct",
h = 1
)
forecast(direct_model)
#> Mixed-frequency forecast
#> -----------------------------------
#> Target series: gdp_growth
#> Forecast horizon: 1
#> Uncertainty: point forecast only
#> -----------------------------------
#> time mean
#> 1 2023-01-01 0.483This is particularly useful at the ragged edge when you want to work only with observed high-frequency information and avoid a separate indicator forecasting step.
Optional Uncertainty Output
By default, bridgr returns point forecasts only. If you
want uncertainty output, request it at estimation time with
se = TRUE and, if needed, custom simulation or full-system
bootstrap controls through bootstrap.
uncertainty_model <- mf_model(
target = gdp_growth,
indic = baro,
indic_predict = "auto.arima",
indic_aggregators = "mean",
target_lags = 1,
h = 4,
se = TRUE,
bootstrap = list(N = 40, block_length = NULL)
)
forecast(uncertainty_model)
#> Mixed-frequency forecast
#> -----------------------------------
#> Target series: gdp_growth
#> Forecast horizon: 4
#> Uncertainty: prediction intervals from residual resampling
#> Simulation paths: 40
#> -----------------------------------
#> time mean se lower_80 upper_80 lower_95 upper_95
#> 1 2023-01-01 0.259 0.746 -0.850 1.230 -1.069 1.638
#> 2 2023-04-01 0.486 1.097 -0.433 1.761 -2.025 3.268
#> 3 2023-07-01 0.500 0.827 -0.441 1.370 -1.174 2.638
#> 4 2023-10-01 0.521 0.884 -0.532 1.731 -1.030 2.191
summary(uncertainty_model)
#> Mixed-frequency model summary
#> -----------------------------------
#> Target series: gdp_growth
#> Target frequency: quarter
#> Forecast horizon: 4
#> Estimation rows: 74
#> Regressors: baro, gdp_growth_lag1
#> -----------------------------------
#> Target equation coefficients:
#> Estimate HAC SE
#> (Intercept) -10.988 3.304
#> baro 0.116 0.033
#> gdp_growth_lag1 -0.316 0.120
#> -----------------------------------
#> Model fit:
#> Statistic Value
#> R-squared 0.572
#> Adjusted R-squared 0.560
#> Residual standard error 0.886
#> -----------------------------------
#> Indicator summary:
#> Frequency Predict Aggregation
#> baro month auto.arima mean
#> -----------------------------------
#> Uncertainty:
#> Coefficient SEs: hac
#> Prediction intervals: residual resampling
#> Simulation paths: 40
#> -----------------------------------
plot(uncertainty_model, type = "forecast")
The uncertainty implementation uses HAC standard errors for the
linear bridge equation, or Delta-HAC standard errors when parametric
aggregation weights are estimated jointly. By default, prediction
intervals are simulated from resampled centered target-equation
residuals. If you also set full_system_bootstrap = TRUE,
bridgr instead uses a full-system target-period block
bootstrap for both coefficient standard errors and prediction intervals,
controlled through
bootstrap = list(N = ..., block_length = ...).
Where to Go Next
The vignette
vignette("mixed-frequency-modeling", package = "bridgr")
compares the main aggregation strategies and shows how
bridgr moves from classic bridge models to unrestricted and
parametric MIDAS-style specifications.
The vignette
vignette("ragged-edge-nowcasting", package = "bridgr")
focuses on indic_predict and the different ways to handle
incomplete high-frequency data at the forecast origin.
The vignette
vignette("uncertainty-and-scenarios", package = "bridgr")
shows how to work with HAC / Delta-HAC coefficient uncertainty,
residual-resampling prediction intervals, the optional full-system
bootstrap, and scenario forecasts based on custom future regressor
paths.