Use when deciding whether a result is the right kind of contribution for Econometric Theory (ET) — a general, foundational econometric-theory result (asymptotics, proofs, theorems) rather than an applied estimate, and whether it fits a full Article or a short Miscellanea.
Scanned 6/5/2026
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---
name: ectheory-topic-selection
description: Use when deciding whether a result is the right kind of contribution for Econometric Theory (ET) — a general, foundational econometric-theory result (asymptotics, proofs, theorems) rather than an applied estimate, and whether it fits a full Article or a short Miscellanea.
---
# Topic Selection (ectheory-topic-selection)
## When to trigger
- You have a new estimator, limit result, or proof and are choosing a target journal
- Unsure whether the result is "ET-foundational" or really an applied/empirical paper
- Deciding between a full **Article** and a short **Miscellanea**
- Wondering whether the result fits one of ET's themed special-issue directions
## The ET fit bar
ET rewards **rigorous, general contributions to the mathematical and statistical foundations of
econometrics** — asymptotic theory, probability-theoretic methods, time-series and nonstationarity
theory, high-dimensional and non-standard environments. The question to answer first: *does this
result teach econometricians something about a method, a limit, or an inferential problem in
generality*, rather than reporting one applied estimate? An empirical application is welcome only as
illustration; the spine must be theory.
Signals of a good ET topic:
- A **new limit theory** (consistency, asymptotic normality, rate, FCLT) under explicitly stated,
defensible regularity conditions
- A method whose **inferential properties** (size, power, robustness, partial identification) are
characterized, not just asserted
- A result that **generalizes** a known special case (weaker assumptions, broader DGP, larger
dimension, dependent data)
- Fit with current ET directions: econometric theory for ML/AI, causal inference under complex
interference, robust inference and partial identification, high-dimensional and non-standard
environments, network/spatial econometrics
## Article vs Miscellanea
- **Article** — multiple major contributions, a new method with extensive proofs, and usually
simulations or an application. Regular Articles must not exceed **50 pages** (overflow → online
Supplement).
- **Miscellanea** — a dedicated venue for **one or two key innovations** stated and proved
concisely; typically **fewer than 20 pages, often ~15**. A clean single theorem that sharpens or
corrects existing theory is a natural Miscellanea.
- The **ET Interview** is an invited oral-history genre, **not** author-submitted — do not target it.
## Checklist
- [ ] The contribution is a general theory result, not a single applied estimate
- [ ] Regularity conditions are statable and defensible (see ectheory-identification-strategy)
- [ ] The generality over prior work is one sentence you can say out loud
- [ ] Article vs Miscellanea chosen, with a length budget against the 50-page ceiling
- [ ] Fit with ET scope / a themed direction articulated
## Anti-patterns
- An applied paper with a thin "theory" wrapper (belongs at an applied outlet)
- A narrow corollary of a known theorem dressed as a full Article (consider Miscellanea or rework)
- Targeting the invited ET Interview genre as if it were submittable
## Output format
```
【Result】one-line statement of the theorem/limit
【ET fit】foundational theory? [Y/N] + why
【Type】Article / Miscellanea + page budget
【Themed fit】direction (if any)
【Next step】ectheory-literature-positioning
```
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