bridgr provides a unified workflow for bridging high-frequency indicators to lower-frequency target series — the practical job at the heart of nowcasting and forecasting macroeconomic variables such as GDP or inflation. It supports classical bridge equations as well as MIDAS-style mixed-frequency regressions (expalmon, beta, unrestricted / U-MIDAS) under a single interface, with automatic frequency alignment, indicator forecasting, and aggregation.
In a typical workflow bridgr takes a low-frequency target (e.g., quarterly GDP) and one or more higher-frequency indicators (e.g., monthly surveys or daily web data), forecasts any missing indicator observations, aggregates them to the target frequency, and fits a regression with optional autoregressive target dynamics. This makes it possible to produce timely predictions well before the official release of the low-frequency data — which is essential for policymakers, central banks, and applied forecasters who need early reads on the macroeconomy.
bridgr supports regular frequency ladders from second up to year, several deterministic and model-based indicator forecasting rules, and multiple aggregation choices including joint expalmon and beta weighting. If a target period contains more high-frequency observations than implied by the current frequency mapping, the package keeps the most recent observations and reports a summarized warning.
Compared with midasr, which is a broad toolkit for estimating, testing, selecting, and forecasting MIDAS regressions, bridgr focuses on end-to-end nowcasting workflows with automatic frequency alignment, indicator extension, aggregation, and a simpler interface from raw series to forecast. Compared with midasml, which targets high-dimensional mixed-frequency time-series and panel models with regularization methods such as sparse-group LASSO, bridgr is aimed at lower-dimensional applied forecasting settings where transparent specification, interpretation, and standard forecasting tooling are the priority.
Installation
From CRAN:
install.packages("bridgr")You can install the development version of bridgr like so:
# install.packages("devtools")
devtools::install_github("marcburri/bridgr")Example
This example estimates a bridge model with uncertainty output:
suppressPackageStartupMessages(library(bridgr))
gdp <- suppressMessages(tsbox::ts_na_omit(tsbox::ts_pc(bridgr::gdp)))
bridge_model <- mf_model(
target = gdp,
indic = baro,
indic_predict = "auto.arima",
indic_aggregators = "mean",
indic_lags = 2,
target_lags = 1,
h = 2,
se = TRUE
)
forecast(bridge_model)
#> Mixed-frequency forecast
#> -----------------------------------
#> Target series: gdp
#> Forecast horizon: 2
#> Uncertainty: prediction intervals from residual resampling
#> Simulation paths: 100
#> -----------------------------------
#> time mean se lower_80 upper_80 lower_95 upper_95
#> 1 2023-01-01 0.808 0.572 -0.075 1.412 -0.265 1.896
#> 2 2023-04-01 0.468 0.678 -0.304 1.343 -0.642 2.788
summary(bridge_model)
#> Mixed-frequency model summary
#> -----------------------------------
#> Target series: gdp
#> Target frequency: quarter
#> Forecast horizon: 2
#> Estimation rows: 72
#> Regressors: baro, baro_lag1, baro_lag2, gdp_lag1
#> -----------------------------------
#> Target equation coefficients:
#> Estimate HAC SE
#> (Intercept) -6.356 1.254
#> baro 0.159 0.031
#> baro_lag1 -0.134 0.049
#> baro_lag2 0.042 0.019
#> gdp_lag1 0.223 0.143
#> -----------------------------------
#> Model fit:
#> Statistic Value
#> R-squared 0.712
#> Adjusted R-squared 0.694
#> Residual standard error 0.747
#> -----------------------------------
#> Indicator summary:
#> Frequency Predict Aggregation
#> baro month auto.arima mean
#> -----------------------------------
#> Uncertainty:
#> Coefficient SEs: hac
#> Prediction intervals: residual resampling
#> Simulation paths: 100
#> -----------------------------------With se = TRUE, bridgr reports HAC or Delta-HAC coefficient standard errors and residual-resampling prediction intervals.
If you want data-driven within-period weights instead of a simple deterministic aggregator, you can switch the indicator aggregator to expalmon. The corresponding weights are estimated jointly within the bridge model, and solver_options let you control the optimization.
expalmon_model <- mf_model(
target = gdp,
indic = baro,
indic_predict = "auto.arima",
indic_aggregators = "expalmon",
solver_options = list(seed = 123, n_starts = 3),
h = 1
)
summary(expalmon_model)
#> Mixed-frequency model summary
#> -----------------------------------
#> Target series: gdp
#> Target frequency: quarter
#> Forecast horizon: 1
#> Estimation rows: 75
#> Regressors: baro
#> -----------------------------------
#> Target equation coefficients:
#> Estimate
#> (Intercept) -9.371
#> baro 0.098
#> -----------------------------------
#> Model fit:
#> Statistic Value
#> R-squared 0.533
#> Adjusted R-squared 0.527
#> Residual standard error 0.913
#> -----------------------------------
#> Indicator summary:
#> Frequency Predict Aggregation
#> baro month auto.arima expalmon
#> -----------------------------------
#> Estimated parametric aggregation:
#> baro weights: 0.006, 0.994, 0.000
#> baro parameters: -4.914, -10.000
#> -----------------------------------
#> Joint parametric aggregation optimization:
#> Method: L-BFGS-B
#> Objective value: 60.832
#> Convergence code: 0
#> Best start: 1 / 3
#> -----------------------------------Code of Conduct
Please note that the bridgr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.