Practical Applications of How Does the Fed Make Decisions: A Machine Learning Augmented Taylor Rule

In How Does the Fed Make Decisions: A Machine Learning Augmented Taylor Rule, published in the Winter 2023 issue of The Journal of Fixed Income, authors Boyu Wu, Asawari Sathe, and Qian Wang of Vanguard and Amina Enkhbold of the Bank of Canada introduce a new four-factor, computer-learning model to predict the federal funds rate set by the Federal Open Market Committee (FOMC). The authors argue that their four-factor model, which considers inflation, labor market conditions, US financial market conditions, and commodity prices (as a proxy for global conditions), outperforms the Taylor rule for predicting the actions of the FOMC.