Identification & Model Logic for RED (red-identification-strategy)

When to trigger

• Establishing why the paper's central claim is credible, before robustness
• Unsure whether RED expects a causal-design argument or a model-assumptions argument
• A computational paper where "identification" means parameter discipline, not instruments

Branch by paper type (RED takes all three)

Theoretical / computational dynamic models

The credibility question is about assumptions, existence, and discipline, not instruments:

• State the model assumptions and regularity conditions explicitly (preferences, technology, stationarity, boundedness, transversality); flag where existence/uniqueness of equilibrium is proved or assumed.
• Make proof exposition clean: state results as propositions, separate assumptions from claims, and put long proofs in an appendix while keeping the intuition in the body.
• Show parameter discipline — which parameters are calibrated to data targets, which are estimated, and which are free; justify each so results are not an artifact of free parameters.
• Discuss generality: what survives relaxing key assumptions, and where the result is knife-edge.

Methodological / computational-method papers

• State the method's regularity conditions and where they bind; characterize accuracy and convergence of the numerical solution; report asymptotics where the method estimates parameters.
• Provide Monte Carlo / numerical experiments that show the method works under known data-generating processes.

Empirical dynamic papers

• Make the causal/identification design explicit (the source of variation, the exclusion logic, the dynamic structure being estimated — e.g., VAR identification, local projections, structural estimation).
• Tie the empirical object back to what it disciplines in the dynamic model.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe it. Full map: execution-with-mcp. RED is quantitative macro — mostly structural/calibration, which is outside this causal-inference toolchain; apply the chain to its empirical/reduced-form papers.

• detect_design → recommend → fit with as_handle=true → audit_result.
• Observational causal claims: staggered DiD (callaway_santanna / sun_abraham + bacon_decomposition + honest_did_from_result); IV (effective_f_test + anderson_rubin_ci); RDD (rdrobust + mccrary_test).
• Experiments: randomization-based inference + romano_wolf for many-outcome control.
• Sensitivity: oster_delta / sensemakr for observational claims.

Report the magnitude in interpretable units; route the full battery to the appendix. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.

Checklist

• The right branch is chosen for the paper type
• Assumptions/conditions (theory) or identifying variation (empirics) are explicit and defended
• Parameter discipline is documented; results are not driven by undisciplined free parameters
• Generality / accuracy / robustness of the core claim is characterized

Anti-patterns

• Importing reduced-form "identification" language into a calibrated model where it does not apply
• Hiding free parameters or equilibrium-existence gaps
• Asserting generality without showing what relaxing the assumptions does

Parameter-discipline table

For quantitative papers, create a table with one row per key parameter:

Any parameter that is free and influential needs a sensitivity check or a narrower claim.

Model-solution audit block

For computational claims, attach an audit record so a referee can see what the numbers rest on:

SOLUTION AUDIT — [model name]
  Method:       EGM on the household problem; sequence-space Jacobian for GE transitions
  State space:  assets 250 pts (log-spaced); productivity 7-state Rouwenhorst
  Convergence:  policy-function sup-norm < 1e-9; market clearing < 1e-7
  Accuracy:     max log10 |Euler error| = -4.3 (off-grid simulation, 100k agents)
  Refinement:   headline counterfactual moves < 0.5% when grids are doubled
  Existence:    stationary-equilibrium existence proved/cited in Appendix A

Any blank line means the matching claim in the text should be weakened until the line can be filled.

Worked discipline review: a search-and-matching draft

A draft calibrates a Diamond–Mortensen–Pissarides economy and claims wage rigidity explains unemployment volatility. Illustrative review of its parameter discipline:

• Matching elasticity 0.5, externally set from the literature — acceptable, but the volatility claim is sensitive to it, so a ±0.15 band belongs in the robustness section.
• Replacement rate 0.71, internally calibrated to market tightness — a RED referee will notice this sits near the Hagedorn–Manovskii region where small match surplus generates volatility mechanically; report the result at a conventional 0.4 as well.
• Rigidity parameter calibrated to the very volatility moment being explained — circular. Move that moment out of the target set, or downgrade "explains" to "is consistent with".

Credibility objections RED referees raise

Supplementary resources

• ../../resources/external_tools.md — solvers and estimation toolkits
• ../../resources/official-source-map.md — scope sources