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格兰杰因果网络和间接反馈
结构 VAR 的非参数变量选择后格兰杰因果网络和间接反馈首先出现在《走向数据科学》上。
来源:走向数据科学过去十年中最常用的计量经济学工作流程之一是使用向量自回归模型。 From research done by academicians to economists informing policy implementation have all utilized VAR models in some shape or iteration [think vector error correcting models(VECM) or Structural VARs (SVAR)]. It could’ve been for impulse response studies, endogenous variable forecasts or something as simple as arguing cross-correlation between temporal variables.
Unfortunately, one problem that VAR studies have not been able to solve for is breaking out the impact of one endogenous variable at a point in time on another into direct,indirect and aggregate feedback. Intuitively, this can be understood by the idea that if one variable affects another directly then we can connect the two explicitly in an arbitrary equation:
但是如果变量只有对另一个变量的中间反馈，我们可以隐式连接它们：
Although we are able to measure aggregate impact through orthogonal impulse-response functions, we cannot still break it out into indirect and direct feedback since that would require us to trace effects through EACH equation in the VAR model .这是一场计算噩梦！
[Readers can be tempted to use SEM models but they also require us to a-priori define relationships within variables and then see how well the data fits our hypothesis;我们在这里试图解决的问题是“首先是什么关系？”]
本文介绍了计量经济学界中一个热门话题：因果网络图。因果图的想法很简单：