This paper analyses the forecasting performance of monetary policy reaction functions using U.S. Federal Reserve's Greenbook real-time data. The results indicate that artificial neural networks are able to predict the nominal interest rate better than linear and nonlinearTaylor rule models as...

This paper introduces a reinforcement learning based approach to compute optimal interest rate reaction functions in terms of fulfilling inflation and output gap targets. The method is generally flexible enough to incorporate restrictions like the zero lower bound, nonlinear economy structures...

We document that expansionary monetary policy shocks are less effective at stimulating output and investment in periods of high volatility compared to periods of low volatility, using a regime-switching vector autoregression. The lower effectiveness of monetary policy can be linked to weaker...

We assess the effects of financial shocks on inflation, and to what extent financial shocks can account for the "missing disinflation" during the Great Recession. We apply a vector autoregressive model to US data and identify financial shocks through sign restrictions. Our main finding is that...