joe-replication-and-data-policy

name	joe-replication-and-data-policy
description	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.

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