Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques

When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. To alleviate the problem, White (2006 White, H. (2006). Approximate nonlinear forecasting methods. In: Elliott, G. Granger, C. W. J. Timmermann, A. eds. Handbook of Economic Forecasting, volume 1, Amsterdam:Elsevier, pp. 459–512.Crossref] , Google Scholar]) presented a solution (QuickNet) that converts the specification and nonlinear estimation problem into a linear model selection and estimation problem. We shall compare its performance to that of two other procedures building on the linearization idea: the Marginal Bridge Estimator and Autometrics. Second, one must decide whether forecasting should be carried out recursively or directly. This choice is investigated in this work. The economic time series used in this study are the consumer price indices for the G7 and the Scandinavian countries. In addition, a number of simulations are carried out and results reported in the article.