A Model for Policy Interest Rates

This paper introduces a model that addresses the key worldwide features of modern monetary policy making: the discreteness of policy interest rates both in magnitude and in timing, the preponderance of status quo decisions, policy inertia and regime switching. We capture them by developing a new dynamic discrete-choice model with switching among three latent policy regimes (dovish, neutral and hawkish), estimated via the Gibbs sampler with data augmentation. The simulations and an application to federal funds rate target demonstrate that ignoring these features leads to biased estimates, worse in- and out-of-sample fit, and qualitatively different inference. Using all Federal Open Market Committee?s (FOMC) decisions made both at scheduled and unscheduled meetings as sample observations, we model the Federal Reserve?s response to real-time data available right before each meeting, and control for the endogeneity of monetary policy shocks. The new model, fitted for Greenspan?s tenure, correctly predicts the directions of about 90% of the next decisions on the target rate (hike, no change, or cut) out of sample during Bernanke?s term including the status quo decisions after reaching the zero lower bound, while the conventional linear model fails to adequately tackle the zero bound and wrongly predicts further cuts.