论文标题
宏观经济空间模型的因果发现
Causal Discovery of Macroeconomic State-Space Models
论文作者
论文摘要
本文介绍了一组测试和一种算法，用于受到DAG结构学习方法启发的宏观经济DSGE模型之间的不可知论，数据驱动的选择。由于对任何DSGE模型的对数线性状态空间解决方案也是一个DAG，因此可以使用相关概念来识别与基于DGP中存在的条件独立关系的唯一基础真相空间模型，该模型与基础DGP兼容。为了对该基地真实模型进行搜索，算法测试了这些条件独立标准的可行类似物，该标准与观察到的变量相对于一组组合可能的状态空间模型。在大型样本中，此过程是一致的。在小样本中，结果可能不是唯一的，因此有条件的独立性测试可以与可能性最大化相结合，以选择单个最佳模型。该算法的疗效用于模拟数据，还提供和讨论了真实数据的结果。
This paper presents a set of tests and an algorithm for agnostic, data-driven selection among macroeconomic DSGE models inspired by structure learning methods for DAGs. As the log-linear state-space solution to any DSGE model is also a DAG it is possible to use associated concepts to identify a unique ground-truth state-space model which is compatible with an underlying DGP, based on the conditional independence relationships which are present in that DGP. In order to operationalise search for this ground-truth model, the algorithm tests feasible analogues of these conditional independence criteria against the set of combinatorially possible state-space models over observed variables. This process is consistent in large samples. In small samples the result may not be unique, so conditional independence tests can be combined with likelihood maximisation in order to select a single optimal model. The efficacy of this algorithm is demonstrated for simulated data, and results for real data are also provided and discussed.
