In the past 10 years, neural networks have emerged as a powerful tool for predictive modeling with "big data." This chapter discusses the potential role of neural networks as applied to economic forecasting. It begins with a brief discussion of the history of neural networks, their use in...

We use a large set of economic and financial indicators to assess tail risks of the three macroeconomic variables: real GDP, unemployment, and inflation. When applied to U.S. data, we find evidence that a dense model using principal components (PC) as predictors might be misspecified by imposing...

Machine learning and artificial intelligence methods are often referred to as \black boxes" when compared to traditional regression-based approaches. However both traditional and machine learning methods are concerned with modeling the joint distribution between endogenous (target) and exogenous...

Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models that exhibit model dependence and have high data demands. We explore deep neural networks as an opportunity to improve upon...

Inequality is widely believed to incite conflict, but causal evidence is scarce. In a mathematical model and an experiment with 1205 human players, we study the effects of inequality and the spatial scale of competition on the evolution of conflict. We show that inequality of outcomes increases...