Forecasting with machine learning Shadow-Rate VARs

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Grammatikopoulos, Michael (2026) Forecasting with machine learning Shadow-Rate VARs. Journal of Forecasting, 45 (2). pp. 770-786. ISSN 0277-6693 (https://doi.org/10.1002/for.70041)

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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.

  • Item type:Article
    ID code:94467
    Dates:
    Date
    Event
    1 March 2026
    Published
    28 October 2025
    Published Online
    25 September 2025
    Accepted
    Subjects:Social Sciences > Economic Theory
    Department:Strathclyde Business School > Economics
    Depositing user: Pure Administrator
    Date deposited:16 Oct 2025 10:48
    Last modified:11 Feb 2026 18:05
    URI:https://strathprints.strath.ac.uk/id/eprint/94467
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