A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks*

We take a model selection approach to the question of whether a class of adaptive prediction models ("artificial neural networks") are useful for predicting future values of 9 macroeconomic variables. We use a variety of out-of-sample forecast-based model selection criteria including forecast error measures and forecast direction accuracy. Ex ante or real-time forecasting results based on rolling window prediction methods indicate that multivariate adaptive linear vector autoregression models often outperform a variety of: (i) adaptive and nonadaptive univariate models, (ii) nonadaptive multivariate models, (iii) adaptive nonlinear models, and (iv) professionally available survey predictions. Further, model selection based on the in-sample Schwarz Information Criterion apparently fails to offer a convenient shortcut to true out-of-sample performance measures. JEL Classification: C22, C51, C53