Causal inference by independent component analysis: Theory and applications

Alessio Moneta*, Doris Entner, Patrik O. Hoyer, Alex Coad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

86 Citations (Scopus)


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).

Original languageEnglish
Pages (from-to)705-730
Number of pages26
JournalOxford Bulletin of Economics and Statistics
Issue number5
Publication statusPublished - 2013 Oct
Externally publishedYes


  • C32
  • C52
  • D21
  • E52
  • L21

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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