PolicyEngine Macro

model 04 — US heterogeneous-agent NK model · us-hank · US · hosted

See who responds to a US shock, not just how much.

Run stylized monetary, fiscal-spending, and productivity shocks through a two-asset heterogeneous-agent New Keynesian model — aggregate impulse responses plus first-order distributional output: MPCs by wealth quartile and the hand-to-mouth share.

how far to trust it

A replication gate, not a forecast-accuracy claim.

The ground truth is the published paper. The model repository's 18-test suite gates the steady-state calibration targets, market clearing, and the signs and magnitudes of the shock responses against the published Econometrica 2021 results; the implementation builds on the authors' own sequence-jacobian toolkit rather than re-deriving the model. Fidelity is strong, the claim narrow: evidence the replication is right, not that the model predicts the US economy — no predictive validation exists or is claimed. The hosted steady state is set against the paper's published calibration targets, value by value, below.

us-hank: steady state vs published calibration targets Table-style chart of the hosted two-asset steady state against the published calibration targets of Auclert, Bardóczy, Rognlie and Straub (2021). Output Y: target 1, achieved 1, deviation 0; Capital K: target 10, achieved 10, deviation 0; Real rate r (quarterly): target 0.0125, achieved 0.0125, deviation 0; Government spending G: target 0.2, achieved 0.2, deviation 0; Government debt Bg: target 2.8, achieved 2.8, deviation 0; Total wealth A+B: target 14, achieved 14, deviation 2.7e-13; Liquid assets B: target 1.04, achieved 1.04, deviation 9.5e-14; Discount factor beta: target 0.976, achieved 0.976274, deviation 2.7e-04. Goods and asset market clearing residuals are 3.3e-08 and 3.1e-13. Beta is the calibrated free parameter that hits the wealth targets. target achieved |deviation| Output Y 1 1 0 Capital K 10 10 0 Real rate r (quarterly) 0.0125 0.0125 0 Government spending G 0.2 0.2 0 Government debt Bg 2.8 2.8 0 Total wealth A+B 14 14 2.7e-13 Liquid assets B 1.04 1.04 9.5e-14 Discount factor beta 0.976 0.976274 2.7e-04 Market clearing: goods 3.3e-08, assets 3.1e-13. Achieved values solved from the hosted adapter; targets from the upstream test suite citing the paper.
Hosted two-asset steady state against the paper's published calibration targets, solved 2026-07-29 at the production grids (nB=50, nA=70, nK=50). β is the calibrated free parameter. Source: papers/us-hank/figures/replication.json.
This validates replication fidelity, not prediction. The model generates deterministic, first-order responses around a calibrated steady state. It does not forecast the US economy, quantify parameter uncertainty, or map detailed tax reforms into its stylized instruments.

An independent replication of a published academic model — not produced or endorsed by the paper's authors. Results are model counterfactuals, not official estimates or forecasts.

For how this model sits among other open-source HANK implementations, see the open US HANK model landscape report. The toolkit it builds on is at shade-econ/sequence-jacobian.

limits

Known limitations.

Known limits of the us-hank implementation
limitdetail
Not a forecaster No data, no estimation, no forecast origin: every result is an impulse response around the paper's calibrated steady state. Use boe-svar (UK) for forecasts; nothing in this suite forecasts the US.
Stylized instruments only Three shock kinds — monetary, fiscal spending, productivity. The labor tax is endogenous (the fiscal block balances the budget): no transfer or tax-rate instrument, and score_reform refuses model="hank" rather than inventing a reform mapping.
First-order responses IRFs are linear in shock size: no state dependence, no zero lower bound, no asymmetry between large/small or positive/negative shocks.
Approximate distributional output The by-quartile consumption response allocates the aggregate response in proportion to steady-state MPCs — the MPC-heterogeneity channel, not full household-level dynamics along the transition.
Calibration, not vintage The parameterisation is the published paper's; there is no data vintage to refresh. Revisiting the calibration would be a deliberate model change, not a data update.