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来源类型Article
规范类型其他
DOI10.1111/j.1468-0084.2012.00710.x
ISBN1468-0084
Causal inference by independent component analysis: theory and applications.
Moneta, Alessio; Entner, Doris; Hoyer, Patrik O; Coad, Alex
发表日期2013-08-16
出处Oxford Bulletin of Economics and Statistics
出版者Wiley-Blackwell
出版年2013
页码705-730
语种英语
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).
特色分类HA Statistics
URLhttp://sro.sussex.ac.uk/id/eprint/40299/
来源智库Science Policy Research Unit (United Kingdom)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/468200
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GB/T 7714
Moneta, Alessio,Entner, Doris,Hoyer, Patrik O,et al. Causal inference by independent component analysis: theory and applications.. 2013.
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