ectheory-identification-strategy — independently scanned and version-tracked by SaferSkills.
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Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
At ET the analogue of an "identification strategy" is a complete, defensible assumption set plus a correct, general proof. The most common ET referee objection is an unstated or implausibly strong regularity condition. Treat the assumption-result-proof triple as the spine:
necessary, can it be weakened, and is it satisfied by a leading example DGP? Distinguish primitive conditions from high-level ones; if you use high-level conditions, show they hold in a concrete case.
distribution, almost surely, uniformly), the rate, and the limiting law (normal, mixed-normal, functional of Brownian motion, non-standard).
probabilistic machinery named (LLN/CLT, triangular-array CLT, FCLT/weak convergence, empirical-process bounds, mixing / near-epoch dependence, concentration inequalities).
non-standard limits, or growing dimension where relevant.
the asymptotic variance explicitly and a consistent estimator of it.
functionals of Brownian motion; care with normalizing rates (e.g., super-consistency).
sparsity or regularization conditions; uniformity over the parameter space.
size control under the least-favorable configuration, and robustness of the inference.
bandwidth/tuning conditions; bias-variance trade-off made explicit.
The single most common Econometric Theory objection is that a regularity condition is too strong or not primitive. Audit each assumption against the columns below before drafting theorems.
| Assumption | Primitive or high-level? | Necessary or convenience? | Holds in a leading example? |
|---|---|---|---|
| Moment / tail | state which | for which CLT/LLN | verify in one DGP (minimal exponent?) |
| Dependence (mixing/NED) | primitive preferred | controls the variance term | e.g., a stable VAR |
| Smoothness / tuning | bandwidth/penalty rate | bias-variance trade | concrete kernel/penalty |
| Identification / rank | primitive on the model | for consistency | a structural example |
A high-level condition with no concrete DGP satisfying it is a classic desk-reject flag.
For beta-hat in a cointegrating regression with a near-integrated regressor (root rho = 1 + c/n): assume a martingale-difference innovation array with finite fourth moments, and prove n(beta-hat - beta) converges to a ratio of stochastic integrals against an Ornstein-Uhlenbeck process via an FCLT plus continuous-mapping, with a separate lemma making the bias o_p(1) uniformly in c — the delicate step. The fixes: "conditions too strong / not primitive" → swap a high-level condition for a primitive moment-plus-dependence pair; "rate without distribution theory" → supply the limiting law (mode, normalizer, functional); "uniformity not established" → isolate it as a named lemma.
【Environment】stationary / nonstationary / high-dimensional / non-standard / semiparametric
【Assumptions】listed + each justified by a leading example? [Y/N]
【Result】object, mode of convergence, rate, limiting law
【Proof plan】roadmap + key lemmas + named tools
【Generality】what is handled beyond the base case
【Gaps】[...]
【Next step】ectheory-contribution-framing~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.