Identification of Structural VAR Models via Independent Component Analysis: A Performance Evaluation Study

Independent Component Analysis (ICA) is a statistical method that transforms a set of random variables in least dependent linear combinations. Under the assumption that the observed data are mixtures of non-Gaussian and independent processes, ICA is able to recover the underlying components, but a scale and order indeterminacy. Its application to structural vector autoregressive (SVAR) models allows the researcher to recover the impact of independent structural shocks on the observed series from estimated residuals. We analyze different ICA estimators, recently proposed within the field of SVAR identification, and compare their performance in recovering structural coefficients. Moreover, after suggesting an algorithm that solve the ICA indeterminacy problem, we assess the size distortions of the estimators in hypothesis testing. We conduct our analysis by focusing on distributional scenarios that get gradually close the Gaussian case, which is the case where ICA methods fail to recover the independent components. In terms of statistical properties of the ICA estimators, we find no evidence that a method outperforms all others. We finally present an empirical illustration using US data to identify the effects of government spending and tax cuts on economic activity, thus providing an example where ICA techniques can be used for hypothesis testing.