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...

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...

The early warning system literature on banking crises has often relied on linear classifiers such as the logit model, which are usually estimated with large datasets of multiple regions of countries. We construct an EWS based on an artificial neural network model with monthly data from the...

We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016, European Journal of Political Economy). Four improvements are particularly notable: First, we produce a...

Virtually each seasonal adjustment software includes an ensemble of seasonality tests for assessing whether a given time series is in fact a candidate for seasonal adjustment. However, such tests are certain to produce either the same resultor conflicting results, raising the question if there...

We use a unique data set that comprises each bank’s bids in the Eurosystem’s main refinancing operations and its recourse to the LOLR facility (a) to derive banks’ willingness-to-pay for liquidity through a one-week repo and (b) to show that a bank’s willingness-to-pay is a good...

Considerable resources have been devoted to gathering data for the measurement of money market activity. However, little is known about the differences between available data and the structural effects of methodological choices. We use the novel dataset MMSR and compare it to data derived from a...

One of the main concerns associated with central bank digital currencies (CBDC) is the disintermediating effect on the banking sector in general, and the risk of bank runs in times of crisis in particular. This paper examines the implications of an interest-bearing CBDC on banking crises in a...