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.
From CRAN:
install.packages("bridgr")You can install the development version of bridgr like
so:
# install.packages("devtools")
devtools::install_github("marcburri/bridgr")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
#> -----------------------------------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.