Forecasting with machine learning Shadow-Rate VARs

Interest rates are fundamental in macroeconomic modeling. Recent studies integrate the effective lower bound (ELB) into vector autoregressions (VARs). This paper studies shadow-rate VARs by using interest rates as a latent variable near the ELB to estimate their shadow-rate values. The study explores machine learning models, such as the Bayesian LASSO, and extends the analysis to include homoscedastic and stochastic volatility shadow-rate VARs. It also examines the integration of shadow rate with vintage-specific long-run assumptions derived from the Survey of Professional Forecasters (SPF). The paper analyzes 16 shadow-rate VARs with 20 US variables, using real-time data from 2005 to 2019 and assesses their predictive accuracy for both point and density forecasts. The findings indicate that shadow-rate models can enhance predictive accuracy for both short-term and longer term horizons across macroeconomic and financial variables. These models could be of use for central banks and policymakers.