Dynamic Asymptotically Ideal Models and Finite Approximation
扩展了Barnett和Jonas的渐近理想模型，使其能处理动态过程生成的数据。通过模拟和货币数据比较动态与静态模型的预测误差，发现AR(1)修正能显著改善低阶有限逼近质量，且仅需多估一个参数。
We extend Barnett and Jonas's asymptotically ideal model (AIM) to model for the possibility that the data were generated by a dynamic process. Prediction errors for dynamic and static AIM models are compared for various simulated datasets. Monetary data are also used to evaluate the AIM specifications. There is substantial evidence that an AR(1) correction considerably improves the quality of low-order finite approximations of AIM with the cost of estimating only one additional parameter. Furthermore, restricting a dynamic AIM to approximate only linear homogenous functions often results in severe misspecification.