The sampling properties of conditional independence graphs for structural vector autoregressions
Marco Reale · Biometrika · 2002
Structural vector autoregressions allow contemporaneous series dependence and assume errors with no contemporaneous correlation. Models of this form, that also have a recursive structure, can be described by a directed acyclic graph. An important tool for identification of these models is the conditional independence graph constructed from the contemporaneous and lagged values of the process. We determine the large‐sample properties of statistics used to test for the presence of links in this graph. A simple example illustrates how these results may be applied.