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red-identification-strategy
GitHub用于构建RED论文可信的推断核心。根据理论、方法或实证动态类型，分别处理模型假设、参数纪律或因果设计。结合StatsPAI工具执行估计与审计，确保结果稳健且逻辑严密。
Trigger Scenarios
确立论文核心主张的可信度
不确定RED要求因果论证还是模型假设论证
计算论文中识别指参数纪律
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill red-identification-strategy -g -y
SKILL.md
Frontmatter
{
"name": "red-identification-strategy",
"description": "Use when making the inferential backbone of a Review of Economic Dynamics (RED) manuscript credible, adapting to the paper type. For theoretical\/computational papers it covers model assumptions, regularity conditions, and what disciplines the parameters; for empirical dynamic papers it covers causal design. RED's scope spans all three, so this skill branches accordingly."
}
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 withas_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_wolffor many-outcome control. - Sensitivity:
oster_delta/sensemakrfor 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:
| Parameter | Value | Source/target | Free or disciplined? | Sensitivity shown? |
|---|
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
| Objection | Branch | Fix |
|---|---|---|
| "A free parameter drives the result" | quantitative | sensitivity table or a narrower claim |
| "Equilibrium existence is assumed silently" | theory | state it as an assumption or prove it |
| "Accuracy not stress-tested at the calibrated point" | computational | Euler/den Haan check at exactly that parameterization |
| "The reduced-form estimate maps to no model object" | empirical | name the structural parameter or moment the estimate disciplines |
Supplementary resources
../../resources/external_tools.md— solvers and estimation toolkits../../resources/official-source-map.md— scope sources
Version History
- 1839142 Current 2026-07-05 14:20
