/build-hank — Build HANK

Category: Build · Primary agent: hank-specialist

Builds a heterogeneous-agent (HANK) model with household block, sequence-space solution and distributional outputs.

Arguments: $ARGUMENTS

Purpose

Build a heterogeneous-agent New Keynesian model: a household block with idiosyncratic income risk and borrowing constraints, solved for its stationary distribution and sequence-space Jacobians, embedded in general equilibrium with firms and policy. The output is a model that delivers both aggregate and distributional impulse responses, matches key micro moments, and passes market-clearing and aggregation checks.

Use it when the distribution of income, wealth or marginal propensities to consume matters for the question: monetary and fiscal transmission through heterogeneity, distributional effects of policy, precautionary saving, inequality dynamics.

Do not use it when a representative-agent model answers the question (/build-dsge) or when static distributional incidence is enough (/distributional-analysis).

Before you start

1. Check required inputs. For each input below record FOUND or ABSENT (never 'assumed'):
   • Approved model-spec with household heterogeneity
   • Micro data targets If a required input is ABSENT, that part is NOT ASSESSED — NO DATA: do not estimate or invent it; name what is missing and which skill produces it. If all required inputs are absent, stop and say so.
2. Check confidentiality. Identify the data classification level of the inputs (.claude/docs/confidentiality-and-ai-use.md). Do not proceed with restricted or confidential data in a way the policy does not allow.
3. Collaboration. Follow .claude/docs/collaboration-protocol.md: ask questions first, present options, get approval before writing files, and update production/session-state/active.md after each approved section.

Agents

• hank-specialist — primary agent; coordinates the work and integrates results
• neural-solver-specialist — Solves high-dimensional economic models with neural networks
• inequality-distribution-specialist — Analyzes inequality and distribution
• numerical-methods-specialist — Specializes in numerical methods

Run these agents as subagents (the Task tool), in parallel where the steps are independent. Subagents only write to the paths named in your instructions.

Key design decisions (present options, let the user choose)

Decision	Typical options	What it changes
Asset structure	one asset, two assets (liquid and illiquid)	MPCs and wealth distribution
Income process	AR(1), richer processes from micro data	Risk and precautionary saving
Nominal rigidities	sticky prices, sticky wages	Transmission and income incidence
Solution method	sequence-space Jacobians, Reiter method	Speed and nonlinearity
Calibration targets	MPCs, wealth shares, liquid wealth, aggregate ratios	Distributional realism

Workflow

1. Implement the household block (endogenous grid method) and test it against known cases
   • Test against analytical cases and a representative-agent limit
2. Compute steady-state distribution and sequence-space Jacobians
   • Check grid convergence and distribution accuracy
3. Assemble general equilibrium; verify market clearing
   • Asset, goods and labor markets clear in steady state and along transitions
4. Calibrate to micro and aggregate moments
   • Targets from household surveys and national accounts; document gaps
5. Produce aggregate and distributional IRFs
   • Decompose consumption responses into direct and indirect effects
6. Register the model as a lego set (/lego-map): a baseplate plus bricks with declared studs (variables, units, timing) and stubs, so components can later be attached, detached or swapped

Checkpoints with the user

• After the household block: confirm asset structure and income process
• After calibration: confirm micro targets and the fit
• After IRFs: confirm the distributional outputs needed

Common pitfalls

• Coarse grids that distort the wealth distribution
• Calibrating MPCs without micro evidence
• Ignoring aggregation errors
• Over-interpreting the distribution from a one-asset model

Output

models/dsge/<name>-hank/ (+ model.lego.json)

The folder holds the household block and tests, steady-state and Jacobian code, the GE assembly, calibration files, the aggregate and distributional IRF pack and the lego manifest. Every run saves its results to output/<run-id>/ in the model folder, with a run log (.claude/rules/model-code.md).

Gate / verdict

• Market clearing and aggregation checks pass
• Micro targets are matched or gaps documented
• Lego manifest passes python tools/lego.py check
• Grid-convergence and accuracy checks are reported
• Distributional IRFs are reported alongside aggregates

Suggested next steps

• /validate-model
• /distributional-analysis
• /lego-map