Beyond Aggregate VARs: A Bayesian Benchmark for HANK Models
Abstract
Heterogeneous-agent New Keynesian (HANK) models characterize how entire cross-sectional distributions respond to structural shocks. Traditional representative-agent models are routinely disciplined by impulse responses from aggregate vector autoregressions (VARs). HANK models have no comparable established empirical benchmark because they make predictions not only about aggregates, but also about distributions of micro-level data. We propose a Bayesian benchmark that jointly models macroeconomic aggregates and several marginal distributions from repeated cross sections, including distributions observed in different surveys. Our approach can use both standard structural VAR identification approaches on macroeconomic aggregates and identification restrictions imposed on micro-level data. The model delivers a joint posterior of the distributional effects of shocks, without the need for household panel data or a separate first-stage density estimate.
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BibTeX
@misc{huber2026beyond,
title = {{Beyond Aggregate VARs: A Bayesian Benchmark for HANK Models}},
author = {Florian Huber and Gary Koop and Christian Matthes},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.06827},
eprint = {2609.06827},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.06827},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Beyond Aggregate VARs: A Bayesian Benchmark for HANK Models
AU - Florian Huber
AU - Gary Koop
AU - Christian Matthes
PY - 2026
DA - 2026/09/06
PB - arXiv
DO - 10.48550/arxiv.2609.06827
UR - https://arxiv.org/abs/2609.06827
AB - Heterogeneous-agent New Keynesian (HANK) models characterize how entire cross-sectional distributions respond to structural shocks. Traditional representative-agent models are routinely disciplined by impulse responses from aggregate vector autoregressions (VARs). HANK models have no comparable established empirical benchmark because they make predictions not only about aggregates, but also about distributions of micro-level data. We propose a Bayesian benchmark that jointly models macroeconomic aggregates and several marginal distributions from repeated cross sections, including distributions observed in different surveys. Our approach can use both standard structural VAR identification approaches on macroeconomic aggregates and identification restrictions imposed on micro-level data. The model delivers a joint posterior of the distributional effects of shocks, without the need for household panel data or a separate first-stage density estimate.
ER -