From ier-skills
Helps refine identification arguments for IER manuscripts: structural parameter ID, causal design, or theory tightness. Does not run estimation.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ier-skills:ier-identificationThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- A structural model's parameters are estimated but it is unclear *what in the data* identifies each one
ier-theory-modelIER prizes a clean model-to-evidence link, so identification is judged by one test: is the mapping from data to the object of interest explicit and defended? The object differs by branch, and so does what "identified" means. Pick the branch and make the mapping transparent — a referee should be able to point at the data feature that moves each estimate.
This is where most IER identification debates happen. The failure mode is treating "the estimator converged" as if it were identification.
ier-theory-model.These two skills divide one question. ier-theory-model asks "is the result tight and general as a theoretical object" — which assumptions are load-bearing, is the comparative static signed. ier-identification asks "is the object recoverable from data" — what moment moves the parameter, what variation supports the causal claim. A structural paper needs both: a tight model whose parameters are also empirically pinned. When a referee says "this is calibration in disguise," that is an identification failure even if the model is theoretically immaculate — route it here, not to ier-theory-model.
For structural papers, the most persuasive single piece of identification evidence is a sensitivity matrix (Andrews–Gentzkow–Shapiro) showing, for each parameter, how its estimate would move if each targeted moment shifted. This converts the abstract claim "the model is identified" into a checkable map: the referee sees that the trade elasticity is driven mainly by the tariff-flow moment, the fixed cost by the extensive-margin moment, and so on. When a parameter's row shows it responds to every moment a little and no moment a lot, that is the data signature of weak identification — and reporting it honestly, with the corrective (a better moment, or a partial-identification statement), is far stronger than hiding it behind a converged objective.
IER's structural readers draw a sharp line between estimated parameters (recovered from data with stated identification) and calibrated/external parameters (set from outside the model). Both are legitimate, but conflating them invites the "calibration in disguise" reject. State explicitly which parameters are estimated and which are external; for each external one, cite the source and carry it into the ier-robustness range analysis. The failure mode is presenting an externally-set parameter as if the model estimated it — referees notice, and the credibility of the whole exercise drops.
A quantitative trade model is estimated and the welfare gain from a tariff cut is the headline. A referee asks what identifies the trade elasticity. A weak answer points at the GMM objective. An IER answer points at a data feature: the elasticity is identified by how bilateral flows respond to plausibly-exogenous tariff variation, and the sensitivity matrix shows that this moment moves the elasticity from, say, 4.0 to 5.5 (illustrative) — making identification visible. Pair it with Monte Carlo recovery (simulated data returns the true elasticity within a few percent) and an untargeted moment the model still matches.
【Journal】International Economic Review
【Skill】ier-identification
【Branch】structural / empirical-causal / econometric-method
【Data-to-object mapping】one sentence: what feature identifies the key object
【Identification evidence】[sensitivity matrix + Monte Carlo / pre-trends+first-stage+density / formal conditions]
【Out-of-sample / falsification】untargeted moments or placebo/falsification shown? [Y/N]
【Counterfactual / external validity】policy-invariance or generalizability argued? [Y/N]
【What it does NOT identify】the object(s) out of reach
【Verdict】credible / needs-work
【Next skill】ier-robustness
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin ier-skillsStress-tests identification strategies for JEEA manuscripts: causal identification in empirical designs, parameter identification in structural models, or assumption transparency in theory papers.
Stress-tests causal identification designs (DiD, IV, RDD, experiment) for EER manuscripts, ensuring credibility before finalizing exhibits.
Stress-tests empirical identification strategies (DID, IV, RDD, event study, structural estimation) for economics manuscripts. Evaluates credibility and economic interpretation before drafting results.