This paper analyses to what extent a selection of leading indicators is able to forecast U.S. recessions, by means of both dynamic probit models and Support Vector Machine (SVM) models, using monthly data from January 1871 to June 2016. The results suggest that the probit models predict U.S....

In this paper, we study the effect of macroeconomic shocks in the determination of house prices. Focusing on the U.S. and the U.K. housing market, we employ time-varying Vector Autoregression models using Bayesian methods covering the periods of 1830-2016 and 1845-2016 respectively. We consider...

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...

The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode...

In this paper we evaluate inflation persistence in the U.S. using long range monthly and annual data. The importance of inflation persistence is crucial to policy authorities and market participants, since the level of inflation persistence provides an indication on the susceptibility of the...