Causal Inference by Independent Component Analysis: Theory and Applications*
介绍一种利用非正态性恢复观测数据因果结构的新方法，并将其应用于微观企业成长与宏观货币政策效果分析，为经济学研究者提供实用工具。
Abstract Structural vector‐autoregressive models are potentially very useful tools for guiding both macro‐ and microeconomic policy. In this study, we present a recently developed method for estimating such models, which uses non‐normality to recover the causal structure underlying the observations. We show how the method can be applied to both microeconomic data (to study the processes of firm growth and firm performance) and macroeconomic data (to analyse the effects of monetary policy).