jru-robustness
GitHub针对《风险与不确定性期刊》规范，按威胁类型组织稳健性检验。涵盖函数形式、激励框架、异质性及多重比较等场景，确保风险参数解释的稳定性，指导执行统计检查并衔接图表生成。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jru-robustness -g -y
SKILL.md
Frontmatter
{
"name": "jru-robustness",
"description": "Use when a Journal of Risk and Uncertainty (JRU) result may be sensitive to specification, incentive frame, sample, or inference. Organizes robustness by the threat to the risk\/uncertainty parameter; it does not invent evidence or citations."
}
Robustness Strategy (jru-robustness)
When to trigger
- The headline risk/ambiguity parameter shifts under a different functional form (CRRA vs. CARA vs. expo-power) and you are unsure which to report
- An experimental result might be an artifact of stakes, order, the random-incentive system, or a particular elicitation device
- A referee will ask whether the finding survives EU vs. non-EU specifications, or pooled vs. heterogeneous-type estimation
- Multiple hypotheses are tested across many lottery menus or treatments and no correction is in place
Organize robustness by threat, not by appendix
A JRU robustness section earns its place when every check is tied to a specific threat to the parameter's interpretation. List the threats first, then the check that answers each.
| Threat to the result | The check that addresses it |
|---|---|
| Functional-form dependence of the risk parameter | Re-estimate under CRRA, CARA, expo-power; report whether the qualitative claim is stable |
| Utility–weighting confound | Show the result holds under a model that separates u from w (e.g., RDU/CPT, not just EU) |
| Elicitation-device artifact | Replicate the pattern with a second device (price list vs. BDM vs. matching probabilities) |
| Random-incentive / isolation failure | Compare one-shot-paid vs. all-paid; test for portfolio/house-money effects |
| Stake / hypothetical-bias sensitivity | Vary real stakes; compare to hypothetical where relevant |
| Subject heterogeneity masked by pooling | Estimate a mixture / finite-type model or random coefficients, not just a representative agent |
| Multiple comparisons across menus/treatments | Adjust (e.g., Holm / Romano–Wolf) and report which results survive |
| Inference too optimistic | Cluster at the subject level; report with few-cluster corrections where needed |
For VSL / insurance empirics, add: alternative risk measures, sample-selection probes, and sensitivity to the publication-selection / meta-analytic benchmark.
Sequencing
- Rank threats by how badly each would damage the headline claim if true.
- Run the check that kills the most dangerous threat first; if the result dies there, stop and rethink before polishing anything.
- Report robustness as "the sign and rough magnitude of [parameter] is stable across [family]," not "Table A12 shows similar results."
- Distinguish checks that the design demands (incentive-frame tests for experiments) from generic ones (alternate clustering).
- Hand off to
jru-tables-figuresonce the parameter is stable across the threats that matter.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JRU spans decision experiments and applied risk; randomization inference for experiments, DiD/IV for observational claims.
- 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
- Every robustness exhibit names the threat it addresses
- The risk parameter is shown stable across at least two functional forms
- A model that separates utility from probability weighting is among the specifications
- Experiments: incentive-frame, stake, and order effects probed; second elicitation device where feasible
- Heterogeneity addressed (mixture / random coefficients) rather than masked by a representative agent
- Multiple-comparison adjustment applied when many menus/treatments are tested
- Inference clusters at the subject (or assignment) level; few-cluster issue handled
Anti-patterns
- A robustness appendix that is a pile of tables with no map from threat to check
- Reporting only the functional form that gives the cleanest number
- Treating an EU-only robustness suite as sufficient when the claim is about non-EU behavior
- Pooling across heterogeneous subjects and presenting the average as if it were a type
- Mining many lottery menus and reporting the significant ones without correction
Distinguish robustness from a specification search
JRU referees draw a sharp line between probing a result and searching for one. Stay on the right side of it:
- Pre-commit the headline specification and present alternatives as deviations from it, not as a menu you chose among.
- Report all the forms you ran, including the ones where the estimate weakened — selective reporting reads as a fishing expedition to a specialist.
- State the decision rule for when the result "survives": e.g., the sign holds and the magnitude stays within a stated band across forms and devices.
- For experiments, distinguish pre-registered confirmatory checks from exploratory ones, and label them as such.
Robustness the experiment specifically demands
Lab and field elicitation papers carry threats that generic econometric robustness misses:
- Comprehension and noise: show the result is not driven by subjects who failed comprehension checks; consider a trembling-hand / Fechner noise term rather than dropping "irrational" subjects.
- Incentive realism: compare real vs. hypothetical, and high vs. low stakes, where the claim depends on it.
- Within-subject consistency: report test-retest or internal consistency for the elicited parameter.
Worked vignette (illustrative)
A paper reports loss aversion λ ≈ 2.1 from a choice-list experiment. The most dangerous threat is that λ is an artifact of the list format (multiple switching, framing). The first check replicates the estimate with a second device (matching probabilities); the second re-estimates under CPT vs. a reference-dependent EU baseline; the third splits by a mixture model to confirm λ is not driven by a confused minority. Only after λ survives all three — with the across-device range reported in full — does the paper present it as the headline in jru-tables-figures.
Output format
【Journal】Journal of Risk and Uncertainty
【Skill】jru-robustness
【Verdict】robust / patch / result fragile
【Top threat】<the check that would most damage the claim>
【Threat→check map】<list>
【Parameter stability】sign+magnitude across <families/devices>
【Heterogeneity】mixture / random coefficients / not addressed
【Source status】verified / 待核实 / not asserted
【Next skill】jru-tables-figures
Version History
- 1839142 Current 2026-07-05 13:57
