restat-identification
GitHub针对REStat期刊，审查DID、RD、IV及测量策略。通过压力测试设计假设、处理交错TWFE、验证工具变量强度及纠正测量误差，确保因果推断与测量质量符合高标准的实证经济学要求。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill restat-identification -g -y
SKILL.md
Frontmatter
{
"name": "restat-identification",
"description": "Use when the causal-identification or measurement strategy is the bottleneck for a The Review of Economics and Statistics (REStat) manuscript — a DID \/ RD \/ IV \/ shift-share design, or a measurement \/ measurement-error problem. Stress-tests the design to REStat's applied-econometrics-and-measurement bar before exhibits are finalized."
}
Identification & Measurement Strategy (restat-identification)
When to trigger
- A causal claim rests on OLS + controls, or TWFE on staggered timing
- An IV's exclusion restriction or first-stage strength is contestable
- An RD's continuity / manipulation assumptions are not yet defended
- A shift-share / exposure design's exogeneity (shares vs shocks) is unargued
- The outcome or key regressor is measured with error, or you built a new measure/index
The REStat identification-and-measurement bar
REStat is applied econometrics with a measurement tradition, so two things are judged together: the mapping from data to the causal object must be explicit and defended, and the quality of measurement behind every variable must be credible. A clean design on a badly measured construct does not clear the bar; neither does a beautifully measured variable in a hopelessly confounded regression. Report standard errors and modern inference; clustering at the assignment level; address attenuation and other measurement-error biases head-on — REStat referees raise measurement objections sibling journals sometimes wave through.
Branch paths
Branch A: Difference-in-differences / event study
- With staggered adoption, move beyond TWFE (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille — the last has REStat-published estimators).
- Show a clean event-study with leads (flat pre-trends) and report a Goodman–Bacon decomposition.
- State the parallel-trends assumption and probe it (pre-trend tests + Rambachan–Roth honest bounds where relevant).
Branch B: Regression discontinuity
- McCrary / Cattaneo–Jansson–Ma density test for manipulation; covariate smoothness at the cutoff.
- Optimal bandwidth + bias-corrected, robust CIs; sensitivity to bandwidth and polynomial order.
- Fuzzy RD: report first stage; defend exclusion of the running variable's other channels.
Branch C: Instrumental variables
- Strong first stage (report effective F / Montiel-Olea–Pflueger); with weak instruments use Anderson–Rubin / weak-IV-robust sets.
- Defend the exclusion restriction in theory, institutions, and falsification tests.
- Shift-share / Bartik: argue exogeneity of shares (Goldsmith-Pinkham–Sorkin–Swift) or of shocks (Borusyak–Hull–Jaravel); report the implied just-identified estimates.
Branch D: Measurement (REStat signature)
- Construct validity: what does the measure actually capture; validate against an external benchmark.
- Measurement error: classical vs non-classical; attenuation correction, validation samples, or bounds.
- New index / data: document construction, sensitivity to choices, and show the applied conclusion is not an artifact of how you measured.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. REStat is applied econometrics/empirical micro — the home of careful identification; DiD/IV/RDD with weak-IV-robust CIs.
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; data-to-object mapping stated in one sentence
- DID: heterogeneity-robust estimator + flat event-study leads + Bacon decomposition
- RD: density test + smoothness + bias-corrected robust CIs + bandwidth sensitivity
- IV: first-stage strength + weak-IV-robust inference + defended exclusion
- Shift-share: exogeneity of shares or shocks argued explicitly
- Measurement: construct validity shown; measurement error addressed (correction / bounds)
- Inference: SEs reported, clustered at the right level; few-cluster issues handled (wild bootstrap)
- The claim never exceeds what identification AND measurement jointly support
Anti-patterns
- TWFE on staggered treatment with no heterogeneity-bias discussion
- An RD with no manipulation test or no bandwidth sensitivity
- A weak first stage reported with conventional t-stats as if robust
- Ignoring attenuation from a noisily measured regressor — a classic REStat referee catch
- A new index presented without validation against any external benchmark
- Conflating "statistically significant" with "credibly identified and well measured"
Worked vignette: a noisily measured regressor (illustrative)
A paper regresses earnings on a survey-reported measure of training hours and finds a small effect. A REStat referee notes the training measure is self-reported and likely error-ridden, biasing the coefficient toward zero. The fix: bring an administrative validation subsample, estimate the reliability ratio (say 0.6, illustrative), and show the attenuation-corrected effect is roughly 1/0.6 larger — turning a "small" effect into an economically meaningful one, with the correction's assumptions stated. Measurement, not just identification, moved the answer.
Output format
【Branch】DID / RD / IV / shift-share / measurement
【Data-to-object mapping】one sentence
【Identification evidence】[event-study+Bacon / density+smoothness / first-stage+AR / shares-or-shocks]
【Measurement evidence】[construct validity / error correction / bounds] — or "n/a, cleanly measured"
【Inference】SEs + clustering level; few-cluster fix if any
【What it does NOT identify】[...]
【Next step】restat-theory-model (or restat-robustness if theory is minimal)
Version History
- 1839142 Current 2026-07-05 14:21
