Searching for the Causal Structure of a Vector Autoregression

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Demiralp, S; Hoover, KD

Published Date

  • December 1, 2003

Published In

Volume / Issue

  • 65 / SUPPL.

Start / End Page

  • 745 - 767

International Standard Serial Number (ISSN)

  • 0305-9049

Digital Object Identifier (DOI)

  • 10.1046/j.0305-9049.2003.00087.x

Citation Source

  • Scopus