Causal Inference by Independent Component Analysis: Theory and Applications*

Oxford Bulletin of Economics and Statistics - Tập 75 Số 5 - Trang 705-730 - 2013
Alessio Moneta1,2,3,4,5, Doris Entner1,2,3,4,5, Patrik O. Hoyer1,2,3,4,5, Alex Coad1,2,3,4,5
19600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.
2Department of Business and Management, Aalborg University, Aalborg, Denmark
3SPRU, University of Sussex, Brighton, UK
4Institute of Economics, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, 56127 Pisa, Italy
5Helsinki Institute for Information Technology, Department of Computer Science, University of Helsinki, Helsinki, Finland

Tóm tắt

AbstractStructural 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).

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Oxford Bulletin of Economics and Statistics

Tập 75 Số 5

705-730

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