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...

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...

This paper examines the role of financial development on U.S. state-level income inequality in the 50 states from 1976 to 2011, using fixed-effect estimation. We find robust results whereby financial development linearly increases income inequality for the 50 states. When we divide 50 states...

This paper employs classical bivariate, factor augmented (FA), slab-and-spike variable selection (SSVS)-based, and Bayesian semi-parametric shrinkage (BSS)-based predictive regression models to forecast US real private residential fixed investment over an out-of-sample period from 1983:Q1 to...

This paper examines the relationship between US crude oil and stock market prices, using a Markov-Switching vector error-correction model and a monthly data set from 1859 to 2013. The sample covers the entire modern era of the petroleum industry, which typically begins with the first drilled oil...