Causal Inference by Independent Component Analysis: Theory and Applications

26 Pages Posted: 17 Aug 2013

See all articles by Alessio Moneta

Alessio Moneta

Scuola Superiore Sant'Anna di Pisa - Laboratory of Economics and Management (LEM)

Doris Entner

Helsinki Institute for Information Technology

Patrik O. Hoyer

University of Helsinki - Department of Computer Science

Alex Coad

CENTRUM Catolica Graduate Business School

Date Written: October 2013

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).

JEL Classification: C32, C52, D21, E52, L21

Suggested Citation

Moneta, Alessio and Entner, Doris and Hoyer, Patrik O. and Coad, Alex, Causal Inference by Independent Component Analysis: Theory and Applications (October 2013). Oxford Bulletin of Economics and Statistics, Vol. 75, Issue 5, pp. 705-730, 2013, Available at SSRN: https://ssrn.com/abstract=2311563 or http://dx.doi.org/10.1111/j.1468-0084.2012.00710.x

Alessio Moneta (Contact Author)

Scuola Superiore Sant'Anna di Pisa - Laboratory of Economics and Management (LEM) ( email )

Piazza Martiri della Liberta', 33-I-56127
Pisa
Italy

Doris Entner

Helsinki Institute for Information Technology ( email )

Helsinki 00180
Finland

Patrik O. Hoyer

University of Helsinki - Department of Computer Science ( email )

Finland

Alex Coad

CENTRUM Catolica Graduate Business School ( email )

1801 Universitaria Avenue
San Miguel
Lima, Lima Lima32
Peru

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