# Joe Replication And Data Policy

> Use to prepare code and data materials for a Journal of Econometrics (JoE) submission under Elsevier's data-citation and availability norms, including reproducible Monte Carlo and the [dataset] reference tag. Reflects that JoE has no mandatory central replication archive — replication is encouraged, not universally mandated.

- Skill: `thedixitjain/joe-replication-and-data-policy` (Agent Skill)
- Install (CLI): `npx skillmds add thedixitjain/joe-replication-and-data-policy`
- Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/joe-replication-and-data-policy/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: thedixitjain (https://skillmd.com/u/thedixitjain)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/thedixitjain/joe-replication-and-data-policy

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# Replication & Data Policy (joe-replication-and-data-policy)

## When to trigger

- You are assembling the code/data materials for a JoE submission or revision
- You need to know whether a mandatory JoE-specific central replication archive is required
- You are citing a dataset and need the correct Elsevier format
- Your Monte Carlo or empirical illustration must be made reproducible for referees

## What JoE actually requires (and does not)

The *Journal of Econometrics* applies **Elsevier's research-data policy**: authors are
**encouraged** to deposit research data in a relevant repository, cite it in the article, and use
Elsevier data-linking / co-submission routes where useful. JoE does not present a
Journal-of-Applied-Econometrics-style mandatory central archive or Econometric-Society-style Data
Editor package as a universal submission requirement in the current Guide for Authors. For JoE,
replication materials for applied **illustrations** should be treated as expected best practice
rather than a named central-archive mandate.

Because JoE is a **methodology** journal, the reproducibility center of gravity is the **Monte Carlo and the estimator code**, not a large administrative-data archive. Make the *method* runnable.

## Data citation (Elsevier `[dataset]`)

- Cite relevant/underlying datasets in the text **and** in the reference list, tagged **`[dataset]`**.
- Elements: **author(s), dataset title, repository, version, year, persistent identifier (DOI)**.
- Include a **data availability statement** describing access conditions for any real data used in the illustration.

## Reproducible methodology package (best practice)

- **Estimator as a usable artifact:** ship the new estimator/test as a documented function or command (R/Stata/Python/MATLAB/Julia) with a minimal worked example so referees can run it.
- **`run_all` master script** that regenerates **every Monte Carlo table, every theory figure, and the empirical illustration** from raw inputs.
- **Pin versions and seeds:** `renv.lock` / `requirements.txt` / recorded `ssc` versions / `Project.toml`; fix and report random seeds and replication counts so simulations reproduce exactly.
- **Archive on a stable repository** (e.g., Zenodo, openICPSR) even though JoE does not name a central archive — it pre-empts referee replication requests and supports the optional **Data in Brief / MethodsX** co-submission route via Editorial Manager.

## Anti-patterns

- Assuming a mandatory, Data-Editor-vetted package like the Econometric Society journals — JoE's current Guide does not name one as a universal requirement
- Citing a dataset only in prose, without the `[dataset]` reference-list entry
- Unreproducible Monte Carlo (unreported seeds, package versions, or replication counts)
- Shipping results but not the estimator, so referees cannot actually run the method


## Reproducibility pass for Journal of Econometrics

Use this as a second-pass capability check. First lock the estimand or theorem, assumptions, asymptotic/simulation evidence, and applied relevance; then test whether the manuscript addresses econometrics reviewers who expect methodological novelty, assumptions, simulation or empirical illustration, and reproducibility.

- **Primary move:** Name data, code, environment, disclosure limits, and archive/deposit route; unresolved proprietary or ethics barriers must be explicit.
- **Decision ledger:** return `claim / evidence / blocker / next edit` rows so the next pass can patch the manuscript directly.
- **Neighbor test:** compare against Econometric Theory for proof-first work, JBES for applied statistical methods, Quantitative Economics for economics-theory methods; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
- **Verification floor:** before submission-ready advice, re-open `resources/official-source-map.md` for volatile rules and name the one unresolved fact that could change the recommendation.

## Output format

```
【Data citation】[dataset] entries with DOI/version? [Y/N]
【Availability statement】access conditions stated? [Y/N]
【Estimator artifact】documented, runnable, worked example? [Y/N]
【run_all】regenerates all MC tables + figures + illustration? [Y/N]
【Reproducibility】seeds + versions + reps pinned? [Y/N]
【Archive】staged on stable repo (optional but recommended)? [Y/N]
【Next step】joe-review-process
```

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**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Journal-of-Econometrics-Skills/skills/joe-replication-and-data-policy/SKILL.md`