Employment is considered a significant determinant of living standards around the globe. For most people, work is the primary source of income, and job-related events and shocks are common causes for individuals and households to escape from poverty or fall into it. Along with employment, it is...

In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric...

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can...

In this paper we propose and examine new approaches in smoothing transition autoregressive (STAR) models. Firstly, a new STAR function is proposed, which is the hyperbolic tangent sigmoid function. Secondly, we propose Feed-Forward Neural Networks Smoothing Transition Autoregressive (FFNN-STAR)...

Purpose – The purpose of this paper is to examine two different approaches in the prediction of the economic recession periods in the US economy.Design/methodology/approach – A logit regression was applied and the prediction performance in two out-of-sample periods, 2007-2009 and 2010 was...

In this paper we present the Radial Basis Neural Network Function. We examine some simple numerical examples of time-series in economics and finance. The forecasting performance is significant superior, especially in financial time-series, to traditional econometric modeling indicating that...