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. Copyright 2003 Blackwell Publishing Ltd.
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Volume (Year): 65 (2003) Issue (Month): s1 (December) Pages: 745-767 Download reference. The following formats are available: HTML
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Bryant, Henry L. & Bessler, David A. & Haigh, Michael S., 2003.
"Causality In Futures Markets,"
Working Papers
28574, University of Maryland, Department of Agricultural and Resource Economics.
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