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ecta-identification
GitHub针对Econometrica论文的识别与渐近理论审查技能。用于在证明前压力测试估计量的识别条件、极限分布及公理基础，确保结构模型的方法论严谨性，涵盖点/集识别、收敛率及非正态极限推导。
Trigger Scenarios
估计量缺失极限分布
断言参数可识别但无证明
理论模型公理未隔离或存在唯一性未证
提出推断但缺乏渐近理论基础
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ecta-identification -g -y
SKILL.md
Frontmatter
{
"name": "ecta-identification",
"description": "Use when the bottleneck is identification and inference for an Econometrica manuscript — identification conditions and asymptotic distribution theory for an estimator, or axioms and existence\/uniqueness for a theory model. Stress-tests the formal foundations before the proofs and simulations are written."
}
Identification, Asymptotics, and Axiomatic Foundations (ecta-identification)
When to trigger
- An estimator is defined and shown consistent, but its limiting distribution is missing
- Identification is asserted ("the parameter is identified") without a proof or counterexample analysis
- A theory model posits behavior but the axioms are not isolated, or existence/uniqueness is unproven
- Inference is proposed (standard errors, tests) without the asymptotic theory that justifies it
This is the formal spine. Econometrica referees check it first; a gap here sinks the paper.
Re-slant for Econometrica. Identification here is not primarily "do I have a credible research design for a causal estimate" (that framing belongs to AER / QJE / JPE / REStud). Econometrica's core is identification and estimator validity inside structural and econometric models — is the structural parameter / functional a one-to-one image of the data distribution, and does the proposed estimator have a derived limiting distribution that licenses its inference? Credible-design content (Branch D) is still in scope for the journal's applied/structural submissions, but the methodological object — completeness, rank, support, the asymptotic law of your estimator — is what carries the paper. Lineage: GMM identification and asymptotics (Hansen 1982), nested fixed-point identification of a dynamic discrete-choice model (Rust 1987), selection-model identification (Heckman 1979).
Branch A — Econometric theory: identification
- Define the parameter / object as a functional of the data-generating process, separate from any estimator. Identification is a property of the population, not the sample.
- State the identification conditions as numbered assumptions (rank / completeness / support / exclusion / monotonicity, as relevant). For each, say what fails without it.
- Prove identification: show the map from distribution to parameter is one-to-one on the admissible class. Where identification can fail, give the explicit failure (partial identification, set identification, point identification under added conditions).
- Distinguish point vs. partial identification. If only set identification holds, define the identified set and characterize it; do not silently assume point identification.
Branch B — Econometric theory: asymptotic distribution theory
- Consistency under stated conditions (which sample sizes / sequences; i.i.d., dependent, or panel asymptotics — be explicit about the regime).
- Rate of convergence — root-n or nonstandard (n^{1/3}, boundary, super-consistent). A nonstandard rate must be derived, not assumed.
- Limiting distribution — derive it; state the asymptotic variance and a consistent estimator of it. If the limit is non-normal (e.g., from a boundary, a non-differentiable moment, or a unit root), characterize it and justify inference accordingly.
- Uniformity — is the asymptotics pointwise or uniform over the parameter space? Modern referees ask for uniform validity (weak-identification-robust, boundary-robust) where the pointwise theory is known to mislead.
- Regularity conditions — smoothness, moment, and bandwidth/tuning conditions stated precisely; primitive where possible rather than high-level.
Branch C — Micro / game / decision theory: axioms and existence/uniqueness
- Isolate the axioms on the primitive (preference relation, choice function, payoff structure). Each axiom should be behaviorally interpretable and stated independently.
- Existence — prove an equilibrium / representation / solution exists (fixed-point, topological, or constructive argument), with the topology and continuity conditions made explicit.
- Uniqueness (or characterization of the set) — prove uniqueness or characterize multiplicity; a representation theorem should pin the functional form up to its known degrees of freedom (e.g., affine transformations of a utility index).
- Independence / tightness of axioms — show no axiom is redundant (each is necessary) and, ideally, that the axiom set is tight (relaxing any one breaks the representation).
- Behavioral payoff — translate the formal result into a statement about observable behavior or comparative statics.
Branch D — Structural / empirical (and credible-design applied)
- State the model's microfoundations and the identifying restrictions explicitly.
- Argue identification of the structural parameters from the available variation (functional form, exclusion, support, instruments) — separate what is identified nonparametrically from what relies on parametric assumptions.
- Provide the estimator's asymptotics or a justified inference procedure; if you use a known estimator off the shelf, cite the precise theorem that licenses your standard errors.
- Counterfactuals must be objects the identification argument actually delivers.
- Credible-design content still belongs here for applied/structural submissions (a DID,
RDD, or IV used inside the paper). But at Econometrica the design alone is not the
contribution — the methodological or identification argument is. If the design is
off-the-shelf and the estimand is the whole point, the paper is general-interest-applied,
not Econometrica (see
ecta-topic-selection).
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. Econometrica publishes econometric theory and applied micro; the chain below serves its applied/empirical papers (weak-IV-robust and modern-DiD reporting expected) — pure theory uses its own apparatus.
detect_design→recommend→ fit withas_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_wolffor many-outcome control. - Sensitivity:
oster_delta/sensemakrfor 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
- Parameter/object defined as a population functional, separate from the estimator
- Identification conditions numbered; each shown to bind (counterexample if dropped)
- Point vs. partial identification stated honestly
- Rate of convergence derived (not assumed), including any nonstandard rate
- Limiting distribution derived; asymptotic variance + consistent estimator given
- Pointwise vs. uniform asymptotics addressed; weak-identification robustness considered
- Regularity conditions primitive and minimal
- (Theory) axioms isolated, independent, behaviorally interpretable; existence + uniqueness proven
Anti-patterns
- "The parameter is clearly identified" with no argument and no failure analysis
- Reporting standard errors with no asymptotic theory justifying them
- Assuming root-n / normality when the moment condition is non-differentiable or on a boundary
- Pointwise asymptotics in a setting (weak IV, near-boundary) where they are known to fail
- High-level "regularity conditions" that quietly assume the hard part
- Axioms that overlap or include a redundant one; existence claimed without a fixed-point argument
- A representation theorem that does not pin the functional form (uniqueness left open)
Output format
【Branch】identification / asymptotics / axioms / structural
【Object/parameter】... (population functional or representation)
【Identification】point / partial — argument: ...
【Rate & limit】rate: ...; limiting distribution: ...; variance estimator: ...
【Uniformity】pointwise / uniform / weak-id-robust
【Regularity conditions】[...] (gaps: [...])
【Next step】ecta-theory-model
Version History
- 1839142 Current 2026-07-05 12:52
