Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. This paper proposes embeddings - a language model trained using machine learning techniques - to locate words and documents in a multidimensional vector space. To accomplish this, we...

The paper formulates the modeling of unconventional monetary policy and critically evaluates its effectiveness to address the Global Financial Crisis. We begin with certain principles guiding general scientific modeling and focus on Milton Friedman's 1968 Presidential Address that delineates the...

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 nonlinear Taylor 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...

The Financial Crisis of 2007-09 caused the U.S. economy to experience a relatively long recession from December 2007 to June 2009. Both the U.S. government and the Federal Reserve undertook expansive fiscal and monetary policies to minimize both the severity and length of the recession. Most...

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...

I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated...

Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. This paper proposes embeddings - a language model trained using machine learning techniques - to locate words and documents in a multidimensional vector space. To accomplish this, we...

During the Global Financial Crisis of 2007-2009, the price of crude oil dropped markedly from about $140 per barrel in June 2008 to about $40 in early 2009. As Quantitative Easing allowed the U.S. economy to stabilize and return to slow growth, oil prices increased and averaged about $100 during...

We study and measure uncertainty in the minutes of the meetings of the board of governors of the Central Bank of Mexico and relate it to monetary policy variables. In particular, we construct two uncertainty indices for the Spanish version of the minutes using unsupervised machine learning...