Causal Inference by Independent Component Analysis: Theory and Applications

被引：102

作者：

Moneta, Alessio ^{[1
]}

Entner, Doris ^{[2
,3
]}

Hoyer, Patrik O. ^{[2
,3
]}

Coad, Alex ^{[4
,5
]}

机构：

[1] Scuola Super Sant Anna, Inst Econ, I-56127 Pisa, Italy

[2] Univ Helsinki, Helsinki Inst Informat Technol, Helsinki, Finland

[3] Univ Helsinki, Dept Comp Sci, SF-00510 Helsinki, Finland

[4] Univ Sussex, SPRU, Brighton, E Sussex, England

[5] Aalborg Univ, Dept Business & Management, Aalborg, Denmark

来源：

OXFORD BULLETIN OF ECONOMICS AND STATISTICS | 2013年 / 75卷 / 05期

基金：

芬兰科学院;

关键词：

C32; C52; D21; E52; L21; PANEL-DATA; VECTOR; COINTEGRATION; SEPARATION; GROWTH; MODELS; TESTS;

D O I：

10.1111/j.1468-0084.2012.00710.x

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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).

引用

页码：705 / 730

页数：26

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