The Phillips Curve and Inflation Expectations: A Machine Learning Perspective

Abstract We employ a novel machine learning framework to assess the evolving transmission of U.S. monetary policy since inflation targeting. Estimating time-varying parameter local projections via ridge regression, our approach captures gradual shifts in macroeconomic relationships and accounts for heteroskedasticity across parameters. Our analysis reveals that the time-varying effects of monetary policy on inflation have strengthened, while the Phillips curve has flattened over much of the pre-pandemic period. The results further indicate a diminished pass-through from short- to long-horizon inflation expectations, consistent with more firmly anchored expectations and improved policy credibility. We also document a temporary steepening of the Phillips curve during the post-pandemic inflation surge, suggesting that Phillips curve dynamics are indeed regime dependent.