| name | aejmac-theory-model |
| description | Use when the quantitative model is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — DSGE, New Keynesian, heterogeneous-agent (HANK / Aiyagari-Bewley), or structural estimation — and calibration, parameter identification, solution accuracy, or counterfactual validity need discipline. For empirical shock identification see aejmac-identification. |
Quantitative Theory & Model Discipline (aejmac-theory-model)
When to trigger
- Parameters are calibrated or estimated but it is unclear what disciplines each one
- A DSGE/HANK model is solved but the solution method / accuracy is unstated
- A counterfactual or welfare number is reported with no validity argument (Lucas critique)
- Untargeted moments are never shown, so the model's fit is asserted not demonstrated
- You are unsure the model clears AEJ: Macro's quantitative-discipline bar
The AEJ: Macro model bar
AEJ: Macro welcomes quantitative-theoretical macro, but the standard is discipline, not decoration: a calibration or structural estimate must be tied to data, the solution must be accurate enough for the claim, and the counterfactual must be defensible. The model exists to deliver a broad-interest macro quantity (a multiplier, a welfare cost, a share of inequality, a propagation magnitude), not to display machinery.
Discipline paths
Path A: Calibration discipline
- Source every parameter. Externally calibrated (cited micro/macro estimates) vs. internally calibrated (matched to targeted moments) — label each and give the target.
- Targeted moments table. Show data vs. model on the moments you matched.
- Untargeted-moment validation. Show the model matches moments it was not asked to match — this is the credibility payoff for calibration.
- Sensitivity. Report how the headline quantity moves with the key parameters (and which moment moves which parameter).
Path B: Structural estimation discipline
- Name what identifies each parameter — the data feature / moment, not "the likelihood." Report a sensitivity / informativeness measure (e.g., a sensitivity matrix) so readers see which data move which parameter.
- Estimator stated (MLE / GMM / SMM / indirect inference / Bayesian) with priors (if Bayesian), starting values, tolerances, and multi-start evidence of a global enough optimum.
- Monte Carlo recovery: simulated data return the true parameters.
Path C: Solution accuracy & numerics
- State the solution method (perturbation order, projection, value-function iteration, sequence-space Jacobian for HANK) and why it suffices for the nonlinearity/size of shock studied.
- For occasionally-binding constraints (ZLB, borrowing limits) or large shocks, justify global vs. local methods.
- Report accuracy diagnostics (Euler-equation errors, grid/refinement checks) where the claim depends on accuracy.
- Set and report seeds for any simulation.
Path D: Counterfactual & welfare validity
- Argue the estimated/calibrated parameters are policy-invariant enough for the counterfactual (Lucas critique); show they are not functions of the policy you change.
- State the welfare metric (consumption-equivalent, etc.) and carry uncertainty into the counterfactual quantity.
- For HANK: be explicit about the distributional channel and the role of the MPC distribution / liquidity.
Checklist
Anti-patterns
- "We calibrate to standard values" with no targets and no sensitivity
- Reporting targeted-moment fit only, never untargeted moments (fit asserted, not validated)
- A first-order perturbation used to study a large nonlinear shock (ZLB, big crisis) without justification
- A welfare/counterfactual number with no policy-invariance argument
- Treating estimation convergence as identification ("the optimizer found a minimum")
- A model with rich machinery but no headline macro quantity a general reader remembers
Worked vignette: disciplining a HANK fiscal multiplier (illustrative)
A HANK model reports a fiscal multiplier of 1.3. A referee asks what disciplines it. The AEJ: Macro answer ties the multiplier to the MPC distribution: the model is calibrated to match the empirical distribution of MPCs (targeted), and then matches the untargeted share of hand-to-mouth households and the consumption response to a transfer from independent micro evidence. A sensitivity check shows the multiplier moves from 1.1 to 1.5 as the liquid-wealth target varies over its empirical range — making visible that the multiplier is governed by liquidity, not a free parameter. Solution by sequence-space Jacobian; Euler-error diagnostics reported (illustrative).
Output format
【Model type】NK-DSGE / HANK / Aiyagari-Bewley / structural-estimation
【Headline quantity】... (with units)
【Parameter discipline】external vs. internal; targeted + untargeted moments
【Identification (structural)】moment ↔ parameter; sensitivity; MC recovery
【Numerics】solution method + why it suffices; accuracy diagnostics; seeds
【Counterfactual validity】policy-invariance + welfare metric + uncertainty
【Next step】aejmac-robustness
