On global identification in structural vector autoregressions

TL;DR

This paper revises the conditions for global identification in structural vector autoregressions, showing that previous rank-based criteria are insufficient and proposing a refined, more accurate set of conditions.

Contribution

It identifies a flaw in the existing rank condition for SVAR global identification and provides a corrected, practical criterion for assessing identification.

Findings

Previous rank condition is not sufficient for global identification.

Counterexample demonstrates redundancy in restrictions can mislead identification.

New criteria improve accuracy of identification assessment in SVARs.

Abstract

In a landmark contribution to the structural vector autoregression (SVARs) literature, Rubio-Ramirez, Waggoner, and Zha (2010, `Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference,' Review of Economic Studies) shows a necessary and sufficient condition for equality restrictions to globally identify the structural parameters of a SVAR. The simplest form of the necessary and sufficient condition shown in Theorem 7 of Rubio-Ramirez et al (2010) checks the number of zero restrictions and the ranks of particular matrices without requiring knowledge of the true value of the structural or reduced-form parameters. However, this note shows by counterexample that this condition is not sufficient for global identification. Analytical investigation of the counterexample clarifies why their sufficiency claim breaks down. The problem with the rank condition is that it allows for the possibility that restrictions are redundant, in the sense that one or more restrictions may be implied by other restrictions, in which case the implied restriction contains no identifying information. We derive a modified necessary and sufficient condition for SVAR global identification and clarify how it can be assessed in practice.