| 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 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
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)
