Model Identification in Dynamic Regression (Distributed Lag) Models
提出一种无需预白化、可直接处理非平稳序列的动态回归模型识别方法，并通过交叉验证因果假设与实证结果来确保模型结构合适，附有示例。
Dynamic regression models (also known as distributed lag models) are widely used in engineering for quality control and in economics for forecasting. In this article I propose a procedure for specifying such models in practice. The proposed procedure requires no prewhitening and can directly handle the nonstationary series. Furthermore, the procedure cross-validates prior beliefs about causal relationships between variables with empirical findings to ensure the suitability of model structure. An illustrative example is given.