Beyond Validity: SVAR Identification Through the Proxy Zoo

TL;DR

This paper introduces a flexible framework for identifying SVAR models using a diverse set of proxy variables, allowing for partial exogeneity and providing tools to assess robustness of empirical conclusions.

Contribution

It develops generalized ranking restrictions (GRR) for SVARs with multiple proxies, enabling partial identification and robustness analysis beyond exact exogeneity assumptions.

Findings

Proxies from sign restrictions can cause bias in estimates.

The proposed framework yields informative, robust identified sets.

Application to U.S. monetary policy demonstrates practical usefulness.

Abstract

This paper develops a framework for robust identification in SVARs when researchers face a zoo of proxy variables. Instead of imposing exact exogeneity, we introduce generalized ranking restrictions (GRR) that bound the relative correlation of each proxy with the target and non-target shocks through a continuous proxy-quality parameter. Combining GRR with standard sign and narrative restrictions, we characterize identified sets for structural impulse responses and show how to partially identify the proxy-quality parameter using the joint information contained in the proxy zoo. We further develop sensitivity and diagnostic tools that allow researchers to assess transparently how empirical conclusions depend on proxy exogeneity assumptions and the composition of the proxy zoo. A simulation study shows that proxies constructed from sign restrictions can induce biased proxy-SVAR estimates, while our approach delivers informative and robust identified sets. An application to U.S.\ monetary policy illustrates the empirical relevance and computational tractability of the framework.