This paper presents a novel approach to analyze human decision-making that involves comparing the behavior of professional chess players relative to a computational benchmark of cognitively bounded rationality. This benchmark is constructed using algorithms of modern chess engines and allows...

We investigate how workers adjust to firms’ investments into new digital technologies, including artificial intelligence, augmented reality, or 3D printing. For this, we collected novel data that links survey information on firms’ technology adoption to administrative social security data....

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...

A key issue in strategic management in the public sector is how government creates economic and social value through procurement. Unfortunately, most procurement studies are based on contract theories, which fail to incorporate the growing role of strategic management in performance. We fill...

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...

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...