From imf-economic-review-skills
Builds a robustness suite for IMF Economic Review manuscripts, checking whether headline cross-country estimates survive specification, sample, country-composition, and inference changes.
How this skill is triggered — by the user, by Claude, or both
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/imf-economic-review-skills:imfer-robustnessThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- The main estimate is in hand and you must show it is not an artifact of one specification
IMFER referees probe whether the headline number is stable, honestly inferred, and not the product of country-composition or specification search — and, distinctively, whether it would still guide policy under reasonable alternatives. Robustness here is a targeted set of checks, each tied to a specific threat to an international-macro design, not a wall of regressions. The persuasive object is that the point estimate barely moves when you drop the obvious country, switch the window, or change the inference.
| Threat to the result | The check that answers it |
|---|---|
| One country / region drives it | leave-one-country-out; drop the dominant economy or region; jackknife |
| A single crisis episode drives it | drop the GFC / euro-crisis / COVID window; alternative event definitions |
| Global common shock confounds it | add the global financial cycle factor / US-shock control; time effects |
| Sample / coverage selection | balanced vs. unbalanced panel; alternative country-inclusion rules; advanced-vs-EM split |
| Specification search | specification curve; pre-registered or primary spec; stepwise controls |
| Measurement convention | alternative deflators, USD vs. local currency, gross vs. net flows |
| Inference too narrow | Driscoll–Kraay (cross-sectional dependence); country clustering; wild-cluster bootstrap (few countries) |
| Omitted confounders | Oster δ / coefficient-stability bounds |
Standard errors are where IMFER robustness most often fails, because country panels violate the assumptions behind default clustering. Flows, spreads, and prices are cross-sectionally dependent (a global shock hits everyone), so country-clustered SEs alone understate uncertainty — use Driscoll–Kraay or a two-way (country and time) cluster. The country dimension is usually small (30–60 economies), so cluster-robust asymptotics are unreliable — report a wild-cluster bootstrap. Serial correlation within country is the norm — do not assume i.i.d. errors. State which of these you address and why; a referee who suspects the SEs are too tight will discount the whole result.
A panel estimate of the spillover from US tightening to EM inflows is −0.8% of GDP (s.e. 0.2). The suite: (i) leave-one-country-out keeps it in [−0.9, −0.7] with no single economy decisive; (ii) dropping the GFC window leaves it at −0.7; (iii) adding the global financial cycle factor barely shifts it; (iv) the AE/EM split shows the effect concentrated in EMs as the mechanism predicts; (v) Driscoll–Kraay SEs (cross-country dependence) keep the CI away from zero; (vi) gross vs. net flows give the same sign and magnitude. The point estimate barely moves — the IMFER target — and the one check that softens it (excluding commodity exporters) is reported with its policy reading.
Because IMFER panels are small in the country dimension and dominated by a few large economies, the single most predictable referee move is "drop the obvious country and show me it survives." Build this into the paper, not the appendix: report a leave-one-country-out distribution (or a coefficient plot with each country excluded), and if one economy is decisive, say so and bound the implication. A panel where China, the US, or one euro-area crisis country silently drives the headline is the most common IMFER robustness failure — and the easiest to pre-empt.
【Journal】IMF Economic Review
【Skill】imfer-robustness
【Primary spec】declared? [Y/N] — estimate: ___ (s.e. ___)
【Composition】leave-one-country-out / drop-dominant range: [___, ___]
【Episode / window】drop-crisis result: ___
【Common shock】global-financial-cycle control result: ___
【Sample / measurement】AE-EM split, currency, gross/net: ___
【Inference】Driscoll–Kraay / wild-cluster (few countries): ___
【Estimate stability】range across checks: [___, ___]; checks that move it: ___
【Next skill】imfer-tables-figures
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin imf-economic-review-skillsDesigns a robustness layer for international-finance results, answering threats like episode-driven results, US-centrism, regime dependence, fragile measurement, and cross-country dependence.
Organizes robustness checks for IER papers by threat to load-bearing assumption, without running regressions. Helps structure responses to referee concerns.
Builds a robustness suite for REStat manuscripts: tests whether headline estimates survive specification, sample, measurement, identification, and inference alternatives.