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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.
From CRAN:
install.packages("bridgr")You can install the development version of bridgr like
so:
# install.packages("devtools")
devtools::install_github("marcburri/bridgr")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.
#> -----------------------------------These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.