Causal Inference by Independent Component Analysis with Applications to Micro- and Macroeconomic Data

摘要: Structural vector-autoregressive models are potentially very useful tools for guiding both macro- and microeconomic policy. In this paper, we present a recently developed method exploiting non-Gaussianity in the data estimating such models, with aim of capturing causal structure underlying data, show how can be applied to (processes firm growth performance) as well macroeconomic (effects monetary policy).