Artificial neural networks (ANNs) are commonly used to solve nonlinear problems. ANNs are composed of parallel interconnected layers that include processing elements called neurons. These neurons use mathematical functions to process the data, mimicking the human brain. In this study, we use...

Machine Learning algorithms, such as the artificial neural networks, are acknowledged to outperform several econometric procedures in predictions. Machine learning becomes popular for doing operations that practically require more efficiency and accuracy, derived basically from the algorithm's...

Central bank intervention in the form of quantitative easing (QE) during times of low interest rates is a controversial topic. This paper introduces a novel approach to study the effectiveness of such unconventional measures. Using U.S. data on six key financial and macroeconomic variables...

This paper contributes a multivariate forecasting comparison between structural models and Machine-Learning-based tools. Specifically, a fully connected feed forward nonlinear autoregressive neural network (ANN) is contrasted to a well established dynamic stochastic general equilibrium (DSGE)...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...