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Ectheory Topic Selection

ASecurity

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.

1,052 stars
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Added 6/5/2026
ai-agentsgo

Security Analysis

A100/100

Scanned 6/5/2026

Install to Claude Code

$npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill ectheory-topic-selection --agent claude-code

Installs into .claude/skills of the current project.

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Files
SKILL.md
---
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
```

Attribution

brycewang-stanfordbrycewang-stanford
View sourceMore from brycewang-stanford →
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