Identifiability of structural singular vector autoregressive models

Abstract

We generalize well-known results on structural identifiability of vector autoregressive (VAR) models to the case where the innovation covariance matrix has reduced rank. Singular structural VAR models appear, for example, as solutions of rational expectation models where the number of shocks is usually smaller than the number of endogenous variables, and as an essential building block in dynamic factor models. We show that order conditions for identifiability are misleading in the singular case and we provide a rank condition for identifiability of the noise parameters. Since the Yule–Walker equations may have multiple solutions, we analyse the effect of restricting system parameters on over- and underidentification in detail and provide easily verifiable conditions.