In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...

It is standard in applied work to select forecasting models by ranking candidate models by their prediction mean square error (PMSE) in simulated ou-of-sample (SOOS) forecasts. Alternatively, forecast models may be selected using information criteria (IC). We compare the asymptotic and...

This paper presents a quarterly global model linking individual country vector errorcorrecting models in which the domestic variables are related to the country-specific foreign variables. The global VAR (GVAR) model is estimated for 26 countries, the euro area being treated as a single economy,...

We empirically analyse the response of US manufacturing labour market variables to various shocks, notably to trade openness and technology. The econometric approach involves an application of the recently developed global VAR (GVAR) methodology of D¶ees, DiMauro, Pesaran, and Smith (2005) to...