We perform non-linearity tests using daily data for leading currencies that include the Australian dollar, British pound, Brazilian real, Canadian dollar, euro, Japanese yen, Mexican peso, and the Swiss franc to resolve the issue of whether these currencies are driven by fundamentals or...

The purpose of this paper is to examine the effectiveness of the policies and procedures towards economic convergence between the countries that participated in the European Exchange Mechanism I and which are now members states of the Eurozone. The question posed is whether the introduction of...

We empirically test the effects of anticipated and unanticipated monetary policy shocks on the growth rate of real industrial production and explicitly test for different types of asymmetries in monetary policy implementation for two major international economies, the U.S. and Brazil. We depart...

We combine signal processing to machine learning methodologies by introducing a hybrid Ensemble Empirical Mode Decomposition (EEMD), Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) model in order to forecast the monthly and daily Euro (EUR)/United States...

We empirically test the effects of unanticipated positive versus negative fiscal policy shocks on the growth rate and the cyclical component of real private output. In doing so, we employed two alternative approaches. The first one uses vector autoregressive systems in order to construct the...

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...

We empirically test the validity of four popular monetary exchange rate models under five alternative inflation expectation approximations using the NOK/USD exchange rate. The selection of Norway seems appropriate as it is a small open economy that does not participate in most economic or...

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...

In this paper, we investigate the ability of two popular models to forecast the deviation of GDP from its long-run trend, i.e. inflationary and output gaps. In doing so, we exploit the information provided by the yield curve that is documented in the literature as a good predictor of economic...