Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques
研究了用神经网络预测宏观经济变量时，三种基于线性化思想的自动模型选择方法（QuickNet、边际桥估计器和Autometrics）的预测效果，并比较了递归预测与直接预测两种策略。
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) 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.