Causal inference by independent component analysis: Theory and applications

Alessio Moneta; Doris Entner; Patrik O. Hoyer; Alex Coad

doi:10.1111/j.1468-0084.2012.00710.x

Causal inference by independent component analysis: Theory and applications

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

Research output: Contribution to journal › Article › peer-review

55 Citations (Scopus)

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).

Original language	English
Pages (from-to)	705-730
Number of pages	26
Journal	Oxford Bulletin of Economics and Statistics
Volume	75
Issue number	5
DOIs	https://doi.org/10.1111/j.1468-0084.2012.00710.x
Publication status	Published - 2013 Oct
Externally published	Yes

Keywords

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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Causal inference by independent component analysis: Theory and applications

Abstract

Keywords

ASJC Scopus subject areas

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