Forward Guidance with Bayesian Learning and Estimation

Considerable attention has been devoted to evaluating the macroeconomic effectiveness of the Federal Reserve's communications about future policy rates (forward guidance) in light of the U.S. economy's long spell at the zero lower bound (ZLB). In this paper, we study whether forward guidance represented a shift in the systematic description of monetary policy by estimating a New Keynesian model using Bayesian techniques. In doing so, we take into account the uncertainty that agents have about policy regimes using an incomplete information setup in which they update their beliefs using Bayes rule (Bayesian learning). We document a systematic change in U.S. policymakers' reaction function during the ZLB episode (2009-2016) that called for a persistently lower policy rate than in other regimes (we call this the forward guidance regime). Our estimates suggest that private sector agents were slow to learn about this change in real time, which limited the effectiveness of t he forward guidance regime in stimulating economic activity and curbing disinflationary pressure. We also show that the incomplete information specification of the model fits economic outcomes over the economy's long spell at the ZLB better than the full information specification.