Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
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jpe-robustness
GitHub针对JPE稿件，构建稳健性检验与反事实逻辑，应对芝加哥学派审稿人质疑。涵盖规范稳健性、机制鉴别及结构有效性，确保结果非偶然且能排除替代解释，支持代码可复现。
Trigger Scenarios
主回归结果仅基于单一模型设定
未排除明显的竞争性经济解释
结构性结果未验证假设扰动下的稳定性
担心审稿人认为结果脆弱或机制识别不清
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jpe-robustness -g -y
SKILL.md
Frontmatter
{
"name": "jpe-robustness",
"description": "Use when the main result of a Journal of Political Economy (JPE) manuscript rests on a single specification and you need to pre-empt the alternative-explanation and fragility objections a Chicago referee will raise. Builds the robustness battery and the falsification logic; it does not establish the primary identification (see jpe-identification)."
}
Robustness & Alternative Explanations (jpe-robustness)
When to trigger
- The headline result is one regression with one set of choices
- You have not ruled out the obvious competing economic explanations
- A structural result has not been shown to survive perturbing key assumptions
- You suspect a referee will say "this is fragile" or "this is mechanism A, not your mechanism B"
The JPE logic of robustness
At JPE, robustness is not a ritual table of "still significant." It is an argument that the economic interpretation survives, and that rival mechanisms are ruled out. A Chicago referee thinks adversarially: which alternative economic story produces the same coefficient, and how do you exclude it? Over-reliance on a single specification is an explicit anti-pattern. And because a conditional accept triggers the JPE Data Editor rerunning your code against the JPE Dataverse deposit (JPE endorses DCAS; see jpe-replication-package), every robustness number must come from code that actually executes and reproduces — fragility you papered over will surface in verification. Distinguish three jobs:
- Specification robustness — the number is not an artifact of arbitrary choices.
- Mechanism discrimination — your channel, not a competing one, drives it.
- External / structural validity — the result generalizes / the model's conclusions are not knife-edge.
What to run
Specification robustness
- Vary controls (parsimonious → saturated); show coefficient stability and use Oster (2019) δ / bounds for selection on unobservables.
- Alternative functional forms, sample windows, and exclusion of influential subsamples.
- Alternative standard-error structures (clustering level, wild bootstrap with few clusters).
- Inference robustness: randomization inference or permutation tests where design allows.
Mechanism discrimination (the JPE-distinctive part)
- Name the 2–3 alternative economic mechanisms that could generate the same reduced-form sign.
- For each, design a test that the alternatives fail and your mechanism passes (heterogeneity that only your channel predicts, an auxiliary outcome, a dose-response the rival cannot explain).
- Triangulate: a second data source, a second identification strategy, or a structural-vs-reduced-form cross-check.
Structural papers
- Sensitivity of estimates and counterfactuals to identifying assumptions and to fixed/calibrated parameters.
- Untargeted-moment fit; over-identification evidence.
- Alternative model specifications that nest or rival the baseline.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JPE is top-5 general-interest economics; a credible design is the entry ticket — modern DiD/IV/RDD and the magnitude for a broad readership.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley. - Re-fit off one handle:
audit_result(result_id)lists missing checks + the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.
Checklist
- Coefficient stability across control sets shown; Oster-style selection bound reported
- Sample-window / outlier / subsample sensitivity reported
- Inference robust to clustering choice / few clusters
- The 2–3 rival economic mechanisms are named and tested against
- At least one triangulation (second data source, design, or structural cross-check)
- Structural results shown not to be knife-edge in key assumptions
- Robustness lives in the paper's appendix/online appendix, with main text stating the punchline
Anti-patterns
- A wall of "still significant" tables that never address why the effect is your mechanism
- Treating robustness as cosmetic while the headline rival explanation goes untested
- Reporting only specifications that work; hiding the fragile ones (a referee will ask, and the JPE Data Editor reruns the code)
- Selection-on-unobservables waved away with "we control for X" and no bound
- Structural counterfactuals presented as point predictions with no sensitivity analysis
- Burying so many checks in the main text that the economic story is lost (use the online appendix)
Output format
【Headline result】coefficient + interpretation
【Spec robustness】[controls, windows, SEs, Oster δ, ...]
【Rival mechanisms】1... 2... — test that discriminates each
【Triangulation】second source / design / structural cross-check
【Structural sensitivity】(if applicable)
【Residual fragility】honest statement of what is not bulletproof
【Next】jpe-tables-figures
Version History
- 1839142 Current 2026-07-05 13:53
