MODEL COMPARISONS IN UNSTABLE ENVIRONMENTS
提出正式检验方法，评估两个错误设定、非嵌套模型在数据不稳定时的样本内相对表现，聚焦局部表现而非全局，并应用于欧元区DSGE模型与VAR的比较。
The goal of this article is to develop formal tests to evaluate the relative in‐sample performance of two competing, misspecified, nonnested models in the presence of possible data instability. Compared to previous approaches to model selection, which are based on measures of global performance, we focus on the local relative performance of the models. We propose tests that are based on different measures of local performance and that correspond to different null and alternative hypotheses. The empirical application provides insights into the time variation in the performance of a representative Euro‐area Dynamic Stochastic General Equilibrium model relative to that of VARs.