Causal Inference by Independent Component Analysis: Theory and Applications*

By Alessio Moneta, Doris Entner, Patrik O. Hoyer, Alex Coad. 2013

205 citations recorded by the source corpus.

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).

Subfields: Analytical Chemistry, Control and Systems Engineering, Signal Processing

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In Signal Processing

Citations in the corpus

GMM Estimation of Non-Gaussian Structural Vector Autoregression (2019, unknown): “Similar results have been put forth in the related literature by Hyv¨arinen et al. (2010), and Moneta et al. (2013), inter alia, but they all assume ε t to be an independent and identically distributed process (instead of being just serially uncorrelated).”
Statistical Inference for Independent Component Analysis: Application to Structural VAR Models (2017, methodology): “In particular it can be used to identify independent shocks in a SVAR model [see e.g. Chen, Choi, Escanciano (2012), Moneta et al. (2013), Gourieroux, Monfort (2014)].”
My first employee: an empirical investigation (2016, unknown): “This has important implications for encouraging entrepreneurs to hire their first employee—not all entrepreneurs should do so, because sales growth seems to precede employment growth in the causal ordering of firm growth processes (Moneta et al. 2013).”