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Forecasting With Machine Learning Shadow‐Rate VARs

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  • Michael Grammatikopoulos

Abstract

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.

Suggested Citation

  • Michael Grammatikopoulos, 2026. "Forecasting With Machine Learning Shadow‐Rate VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(2), pages 770-786, March.
    • Handle: RePEc:wly:jforec:v:45:y:2026:i:2:p:770-786
    DOI: 10.1002/for.70041
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