qe-identification-strategy
GitHub针对定量经济学手稿的识别策略进行压力测试，涵盖结构模型、因果推断及实验设计。确保数据到目标对象的映射清晰，满足计量经济学会期刊标准，包括参数识别、稳健性检验及可复现性要求。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill qe-identification-strategy -g -y
SKILL.md
Frontmatter
{
"name": "qe-identification-strategy",
"description": "Use when the identification argument is the bottleneck for a Quantitative Economics (QE) manuscript — whether causal identification in an empirical design, parameter identification in a structural\/computational model, or treatment-effect identification in an experiment. Stress-tests the strategy to the QE general-interest quantitative bar before exhibits are finalized."
}
Identification Strategy (qe-identification-strategy)
When to trigger
- A structural model's parameters are estimated but it is unclear what in the data identifies them
- An empirical causal claim rests on OLS + controls, or TWFE on staggered timing
- An experiment's estimand or its assumptions are not pinned down
- You are unsure the identification clears QE's quantitative, general-interest bar
The QE identification bar
QE is the Econometric Society's empirical/quantitative general-interest journal, so identification is judged through an ES lens: the mapping from data to the object of interest must be explicit and defended, whatever the method. Because QE spans empirical, structural/computational, experimental, and simulation work, "identification" means different things by branch — pick the branch and make the argument transparent. QE's house norms reinforce this: report standard errors and confidence/coverage sets (never significance asterisks), and make the strategy reproducible for the pre-acceptance ES Data Editor check.
Branch paths
Branch A: Structural / computational identification
- Name what identifies each parameter. Tie parameters to specific data features / moments; argue identification from the model's structure, not just "the estimator converged."
- Targeted vs. untargeted moments: report fit to targeted moments and show untargeted-moment validation as out-of-sample discipline.
- Sensitivity / informativeness: report parameter sensitivity to moments (e.g., a sensitivity matrix) so readers see which data move which parameters.
- Estimation regularity: state the objective (MLE / GMM / MSM / indirect inference), starting values, tolerances, and that the optimum is global enough (multi-start). Report Monte Carlo evidence that the procedure recovers known parameters.
- Counterfactual validity: argue the estimated parameters are policy-invariant enough for the counterfactual you run.
Branch B: Empirical causal design (applied micro / finance)
- DID / event study: with staggered adoption move beyond TWFE (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille); show a clean event-study with leads; report a Goodman-Bacon decomposition.
- IV: strong first stage; with weak instruments use Anderson–Rubin / weak-IV-robust sets; defend the exclusion restriction in theory, institutions, and falsification.
- RDD: McCrary / Cattaneo–Jansson–Ma density test; optimal bandwidth + robustness; covariate smoothness; bias-corrected CIs.
- Inference clustered at the assignment level; address few-cluster issues (wild-cluster bootstrap).
Branch C: Experimental
- Pre-registration in a recognized registry (AEA RCT Registry / AsPredicted / OSF) — required for own-data studies effective Jan 1, 2026; report deviations.
- Detailed instructions / survey transcripts included at initial submission.
- Randomization balance; attrition (Lee bounds if differential); multiple-hypothesis adjustment; explicit estimand and external-validity discussion.
Branch D: Simulation / measurement
- Documented data-generating process; seeds set and reported.
- Show the measured object is robust to grid/tuning choices and disciplined against measurement error and alternatives.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. Quantitative Economics spans structural and applied micro; the chain serves its reduced-form lane, structural estimation uses its own toolkit.
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
- Branch chosen; the data-to-object mapping stated in one sentence
- Structural: each parameter tied to identifying moments; sensitivity + Monte Carlo recovery shown
- Empirical: design-appropriate diagnostics (pre-trends / density / first-stage / balance); modern estimator where TWFE would bias
- Experimental: pre-registered; instructions included; balance/attrition/MHT handled
- Inference reported as SEs / coverage sets (no asterisks); clustering/assignment level correct
- The claim never exceeds what the identification supports
Anti-patterns
- "The estimator converged" presented as if it were identification (structural)
- TWFE on staggered treatment with no heterogeneity-bias discussion (empirical)
- Calibrating parameters and running a counterfactual without arguing policy-invariance
- An experiment with no pre-registration or no reported estimand
- Reporting significance with asterisks instead of standard errors / coverage sets
Worked vignette: identifying a search-cost parameter (illustrative)
A labor-search model is estimated on matched employer–employee data. The referee asks what identifies the search-cost parameter. A weak answer points at the likelihood; a QE answer points at a data moment: the elasticity of the job-finding hazard to local vacancy density pins the cost, because a steeper hazard maps to a lower cost. Suppose the sensitivity matrix shows a 0.4 elasticity moves the estimate from 0.9 to 0.6 — that number makes identification visible. Pair it with Monte Carlo recovery (simulated panels return the true cost within 5%, illustrative).
Referee pushback mapped to the identification fix
- "Estimates are not credibly identified — calibration in disguise." → Show the sensitivity matrix and which moment moves which parameter; report untargeted fit.
- "Your counterfactual assumes policy-invariant parameters you never defend." → Argue invariance (Lucas critique); show parameters are not functions of the policy.
- "Staggered TWFE here is biased." → Re-estimate with Callaway–Sant'Anna or Sun–Abraham; show flat event-study leads.
Output format
【Branch】structural / empirical / experimental / simulation
【Data-to-object mapping】one sentence
【Identification evidence】[moments+sensitivity / pre-trends+density+first-stage / balance / DGP]
【Estimation/inference】objective + SEs/coverage (no asterisks); clustering if any
【What it does NOT identify】[...]
【Next step】qe-data-analysis
Version History
- 1839142 Current 2026-07-05 14:17
