Searching for the Causal Structure of a Vector Autoregression*

We provide an accessible introduction to graph-theoretic methods for causal analysis. Building on the work of Swanson and Granger (Journal of the American Statistical Association, Vol. 92, pp. 357–367, 1997), and generalizing to a larger class of models, we show how to apply graph-theoretic methods to selecting the causal order for a structural vector autoregression (SVAR). We evaluate the PC (causal search) algorithm in a Monte Carlo study. The PC algorithm uses tests of conditional independence to select among the possible causal orders – or at least to reduce the admissible causal orders to a narrow equivalence class. Our findings suggest that graph-theoretic methods may prove to be a useful tool in the analysis of SVARs. I. The problem of causal order Drawing on recent work on the graph-theoretic analysis of causality, we propose and evaluate a statistical procedure for identifying the contemporaneous causal order of a structural vector autoregression. *We thank Marcus Cuda for his help with programming and computational design, Derek Stimel and Ryan Brady for able research assistance, and to Oscar Jorda, Stephen Perez, and the participants in the European Community Econometrics Conference (EC), University of Bologna, Italy, 13–14 December 2002, for comments. The views expressed do not necessarily reflect the views of the Federal Reserve System. JEL Classification numbers: C30, C32, C51. OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 65, SUPPLEMENT (2003) 0305-9049