The difficulty in modelling inflation and the significance in discovering the underlying data generating process of inflation is expressed in an ample literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting the...

In this paper, we present a forecasting model of bank failures based on machine-learning. The proposed methodology defines a linear decision boundary separating the solvent from the failed banks. This setup generates a novel alternative stress testing tool. Our sample of 1443 U.S. banks includes...

Purpose -- This study presents an empirical model designed to forecast bank credit ratings using only quantitative and publicly available information from their financial statements. For this reason we use the long term ratings provided by Fitch in 2012. Our sample consists of 92 U.S. banks and...

In this paper, we evaluate the causal relationship between macroeconomic uncertainty indices, inflation and growth rate for 17 Eurozone countries on a county level examination. In performing a series of linear and non-linear causality tests we find little evidence of a causal relationship...

This study performs a long-run, inter-temporal analysis of income inequality in the U.S. for the period 1916-2012. We use a) descriptive analysis to examine the evolution of inequality through time and b) complex network tools, more specifically an optimization technique called the...