Bayesian Vector Autoregression for Joint Macroeconomic Forecasting: Pseudo-Real-Time Evidence, Density Forecasts, Structural Dynamics, Scenario Analysis, and Practical Macro-Risk Use for the United States

Citation

Authors: Cenk Ufuk Yildiran
Publication: SSRN working paper, posted 27 September 2026 (77 pages).
Type: SSRN working paper

Research overview

This working paper develops and evaluates a compact Bayesian vector autoregression for joint U.S. forecasting of real GDP growth, core PCE inflation, unemployment, and the federal funds rate. Its pseudo-real-time evaluation examines density and point forecast performance, calibration, model-selection uncertainty, propagation diagnostics, and scenario analysis. The SSRN record is the authoritative source for the complete abstract and paper.

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• Official publication or repository record
• DOI: 10.2139/ssrn.7532698
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