bridgr is designed to simplify the implementation and evaluation of bridge models, which are useful for nowcasting (predicting the present or near-term) and forecasting macroeconomic variables like GDP.
Bridge models are statistical tools that link high-frequency indicators (e.g., monthly industrial production) to low-frequency target variables (e.g., quarterly GDP) by forecasting and aggregating the indicators to match the target’s frequency. They enable timely predictions before the official release of low-frequency data, making them essential for policymakers who need early insights for decision-making.
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 is a basic example:
library(bridgr)
gdp <- suppressMessages(tsbox::ts_pc(bridgr::gdp))
bridge_model <- bridge(
target = gdp,
indic = baro,
indic_predict = "auto.arima",
indic_lags = 2,
target_lags=1,
h=2
)
#> The start dates of the target and indicator variables do not match. Aligning them to 2004-04-01
#> Dependent variable: gdp | Frequency: quarter | Estimation sample: 2004-04-01 - 2022-10-01 | Forecast horizon: 2 quarter(s)
forecast(bridge_model)
#> Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
#> 74 0.8313868 -0.1302710 1.793045 -0.6393418 2.302115
#> 75 0.5363317 -0.4397745 1.512438 -0.9564939 2.029157
summary(bridge_model)
#> Bridge model summary
#> -----------------------------------
#> Main model:
#> -----------------------------------
#> Series: gdp
#> Regression with ARIMA(1,0,0) errors
#>
#> Coefficients:
#> ar1 intercept baro baro_lag1 baro_lag2
#> 0.1740 -7.4164 0.1574 -0.0957 0.0172
#> s.e. 0.1312 1.4152 0.0126 0.0125 0.0127
#>
#> sigma^2 = 0.5631: log likelihood = -80.04
#> AIC=172.09 AICc=173.36 BIC=185.83
#> -----------------------------------
#> Single indicator models:
#> -----------------------------------
#> Series: baro
#> ARIMA(1,0,2) with non-zero mean
#>
#> Coefficients:
#> ar1 ma1 ma2 mean
#> 0.6688 0.5305 0.3316 100.8580
#> s.e. 0.0653 0.0799 0.0753 1.5774
#>
#> sigma^2 = 18.46: log likelihood = -646.14
#> AIC=1302.28 AICc=1302.55 BIC=1319.36
#> Aggregation to low frequency:
#> Using mean over values in corresponding periods.
#> -----------------------------------