Algorithm-Driven SVARs: Navigating the Wilderness of Big Data

2026-08-05

AI Summary

This study addresses the sensitivity of traditional structural vector autoregressive (SVAR) models to ad hoc variable selection by proposing a Bayesian framework that incorporates an algorithm-driven variable selection mechanism. The approach integrates recursive identification with a Bayesian SVAR, an anchor-free joint proxy model, and a multi-instrument strategy, enabling automatic construction and optimization of the information set via out-of-sample criteria. This method preserves the largest feasible system while enhancing model robustness and interpretability. Empirical results reveal that housing production—not household credit—is the primary driver of output expansion. Furthermore, in monetary policy transmission, the credit spread channel is markedly amplified, with corporate default risk emerging as a critical margin.

Abstract

Every SVAR result is conditional on two choices: the restrictions that identify the shock and the variables on which they operate. The literature disciplines the first; the second is chosen by hand. We develop a Bayesian methodology that constructs information sets, uses an out-of-sample criterion, and retains the largest system it admits. Under recursive identification, output rises with housing production rather than household credit alone. For monetary policy, an anchor-free joint Bayesian proxy SVAR with multiple instruments strengthens the credit spread channel. A core system augmented with the selected corporate spread identifies expected default risk as a potent transmission margin.

Problem

• SVAR
• variable selection
• big data
• Bayesian methodology
• information set

Innovation

• Bayesian SVAR
• algorithm-driven selection
• out-of-sample criterion
• proxy SVAR
• information set construction