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

60 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

Access to Document

10.1111/j.1468-0084.2012.00710.x

Cite this

@article{5b921e5a26cb4e98bd25007f429caf2f,

title = "Causal inference by independent component analysis: Theory and applications",

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).",

keywords = "C32, C52, D21, E52, L21",

author = "Alessio Moneta and Doris Entner and Hoyer, {Patrik O.} and Alex Coad",

year = "2013",

month = oct,

doi = "10.1111/j.1468-0084.2012.00710.x",

language = "English",

volume = "75",

pages = "705--730",

journal = "Oxford Bulletin of Economics and Statistics",

issn = "0305-9049",

publisher = "Wiley-Blackwell",

number = "5",

}

TY - JOUR

T1 - Causal inference by independent component analysis

T2 - Theory and applications

AU - Moneta, Alessio

AU - Entner, Doris

AU - Hoyer, Patrik O.

AU - Coad, Alex

PY - 2013/10

Y1 - 2013/10

N2 - 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).

AB - 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).

KW - C32

KW - C52

KW - D21

KW - E52

KW - L21

UR - http://www.scopus.com/inward/record.url?scp=84882287602&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84882287602&partnerID=8YFLogxK

U2 - 10.1111/j.1468-0084.2012.00710.x

DO - 10.1111/j.1468-0084.2012.00710.x

M3 - Article

AN - SCOPUS:84882287602

VL - 75

SP - 705

EP - 730

JO - Oxford Bulletin of Economics and Statistics

JF - Oxford Bulletin of Economics and Statistics

SN - 0305-9049

IS - 5

ER -

Causal inference by independent component analysis: Theory and applications

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this