Toward more accurate macroeconomic forecasts
提出一种统计方法，用于确定向量自回归模型中解释变量的最佳配置，使预测比传统VAR模型更准确，并减少模型设定中的人为判断。
A growing disenchantment with conventional economic models has resulted in increased interest in forecasting with vector autoregressive (VAR) models. In this article, Roy H. Webb develops a statistical procedure for determining the best configuration of explanatory variables in the equations of a VAR model. The resulting model forecasts more accurately than a conventional VAR model and is comparable to VARs improved through other popular methods. In addition, Webbs procedure lets the data determine the form of the model and reduces the role of judgment in specifying equations, consistent with the atheoretical spirit of VAR models.