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Algorithm-Driven SVARs: Navigating the Wilderness of Big Data

Yucheng Yang, Tao Zha

arXiv 5 Aug 2026 · Econometrics

arXiv:2608.05017 · PDF · Extracted main text

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.

Citation extraction

35
references
62
in-text mentions
35
distinct cited
6
self-citations
23,760
main-text words

appendix boundary found by appendix_command · 75% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Eric T. Swanson (2020) Measuring the effects of federal reserve forward guidance and asset purchases on financial markets1.00083100%
2Edward E. Leamer (2007) Housing is the business cycle0.92843100%
3Edward E. Leamer (2015) Housing really is the business cycle: What survives the lessons of 2008–09?0.92843100%
4Dario Caldara and Edward Herbst (2019) Monetary policy, real activity, and credit spreads: Evidence from Bayesian proxy SVARs0.81142100%
5Shihao Gu, Bryan Kelly, and Dacheng Xiu (2020) Empirical asset pricing via machine learning0.73732100%
6Daniel F. Waggoner and Tao Zha (2003) A gibbs sampler for structural vector autoregressions self0.73732100%
7Domenico Giannone, Michele Lenza, and Giorgio E. Primiceri (2021) Economic predictions with big data: The illusion of sparsity0.64422100%
8Karsten Müller and Emil Verner (2024) Credit allocation and macroeconomic fluctuations0.64422100%
9Matthew Rognlie, Andrei Shleifer, and Alp Simsek (2018) Investment hangover and the great recession0.64422100%
10Christopher A. Sims and Tao Zha (1999) Error bands for impulse responses self0.64422100%

Showing the top 10 of 35 scored citations.