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JMLR: Workshop and Conference Proceed<strong>in</strong>gs 12:95–114, 2011<strong>Causality</strong> <strong>in</strong> <strong>Time</strong> <strong>Series</strong>Causal Search <strong>in</strong> Structural Vector Autoregressive ModelsAlessio MonetaMax Planck Institute of EconomicsJena, GermanyNad<strong>in</strong>e ChlaßFriedrich Schiller University of Jena, GermanyDoris EntnerHels<strong>in</strong>ki Institute for Information Technology, F<strong>in</strong>landPatrik HoyerHels<strong>in</strong>ki Institute for Information Technology, F<strong>in</strong>landmoneta@econ.mpg.denad<strong>in</strong>e.chlass@uni-jena.dedoris.entner@cs.hels<strong>in</strong>ki.fipatrk.hoyer@hels<strong>in</strong>ki.fiEditors: Flor<strong>in</strong> Popescu and Isabelle GuyonAbstractThis paper reviews a class of methods to perform causal <strong>in</strong>ference <strong>in</strong> the framework ofa structural vector autoregressive model. We consider three different sett<strong>in</strong>gs. In thefirst sett<strong>in</strong>g the underly<strong>in</strong>g system is l<strong>in</strong>ear with normal disturbances and the structuralmodel is identified by exploit<strong>in</strong>g the <strong>in</strong>formation <strong>in</strong>corporated <strong>in</strong> the partial correlationsof the estimated residuals. Zero partial correlations are used as <strong>in</strong>put of a searchalgorithm formalized via graphical causal models. In the second, semi-parametric,sett<strong>in</strong>g the underly<strong>in</strong>g system is l<strong>in</strong>ear with non-Gaussian disturbances. In this casethe structural vector autoregressive model is identified through a search procedurebased on <strong>in</strong>dependent component analysis. F<strong>in</strong>ally, we explore the possibility ofcausal search <strong>in</strong> a nonparametric sett<strong>in</strong>g by study<strong>in</strong>g the performance of conditional<strong>in</strong>dependence tests based on kernel density estimations.Keywords: Causal <strong>in</strong>ference, econometric time series, SVAR, graphical causal models,<strong>in</strong>dependent component analysis, conditional <strong>in</strong>dependence tests1. Introduction1.1. Causal <strong>in</strong>ference <strong>in</strong> econometricsApplied economic research is pervaded by questions about causes and effects. Forexample, what is the effect of a monetary policy <strong>in</strong>tervention? Is energy consumptioncaus<strong>in</strong>g growth or the other way around? Or does causality run <strong>in</strong> both directions? Areeconomic fluctuations ma<strong>in</strong>ly caused by monetary, productivity, or demand shocks?Does foreign aid improve liv<strong>in</strong>g standards <strong>in</strong> poor countries? Does firms’ expenditure<strong>in</strong> R&D causally <strong>in</strong>fluence their profits? Are recent rises <strong>in</strong> oil prices <strong>in</strong> part caused byspeculation? These are seem<strong>in</strong>gly heterogeneous questions, but they all require someknowledge of the causal process by which variables came to take the values we observe.c○ 2011 A. Moneta, N. Chlaß, D. Entner & P. Hoyer.
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