jme-replication-and-data-policy - SKILL.md Agent Skill

name: jme-replication-and-data-policy description: Use when assembling the supplementary-materials / replication deposit for a Journal of Monetary Economics (JME) manuscript — depositing data, code (Dynare/MATLAB/Stata/R), and appendices on ScienceDirect / Mendeley Data per JME's supplemental-materials policy, plus the Elsevier generative-AI declaration.

Replication & Data Policy (jme-replication-and-data-policy)

When to trigger

The paper is empirical/quantitative and you must prepare a deposit
An editor has asked for supplemental materials as a condition of publication
You want to pre-empt referee replication concerns
You need to know exactly what JME's data policy does and does not require

What JME's policy actually is

JME maintains a supplementary-materials / replication policy rather than a one-size mandatory verification gate. Precisely:

An editor may require provision of supplemental materials — proofs, data sets, and computational programs — as a condition of publication.
Authors are strongly advised to deposit appendices, computer programs, and data files on ScienceDirect (and Mendeley Data is supported) to enhance replication of and citation to the research.
Authors must include the declaration of any use of generative AI in manuscript preparation, per Elsevier policy.

Note what is 待核实: a separately enforced, AEA/Econometrica-style mandatory pre-publication data-and-code-availability verification workflow could not be confirmed from an official JME page. The confirmed expectation is a strong deposit recommendation plus editor discretion to require materials. Treat any claim of a formal verified-reproducibility check as unverified, and build the package as if it will be required — because the editor can require it.

What to deposit

Data: raw and constructed data sets (or, for proprietary/licensed series, the construction code plus access instructions).
Code: estimation and model code — Dynare .mod files, MATLAB/Julia DSGE solvers, Stata/R scripts for VAR/LP — with recorded software and package versions.
A master script (run_all) that regenerates every table, figure, and IRF from raw inputs.
Appendices: the online supplementary appendix (exempt from the 40-page cap) and a README documenting steps, seeds, and runtime.

Checklist

Data and/or construction code deposited (proprietary series handled with access notes)
Estimation/model code included (Dynare/MATLAB/Julia/Stata/R) with versions pinned
run_all master script reproduces all exhibits, including IRFs and FEVDs
Seeds for MCMC / bootstrap / simulation set and reported
README documents inputs, steps, runtime, and software versions
Materials staged for ScienceDirect / Mendeley Data deposit
Generative-AI declaration included per Elsevier policy

Anti-patterns

Assuming no deposit is needed because there is no AEA-style mandatory verifier (the editor can still require it)
Submitting final-revised data only, with no code to reconstruct real-time series
A Dynare/MATLAB package that does not run end-to-end on a clean machine
Omitting the generative-AI declaration

Reproducibility pass for Journal of Monetary Economics

Use this as a second-pass capability check. First lock the main macro object, the identifying variation, and the policy-relevant counterfactual; then test whether the manuscript addresses macro and monetary economists who expect the shock, mechanism, and policy margin to be visible early.

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 JIE for open-economy trade/finance emphasis, RED for dynamic macro theory, AEJ Macro for broader field positioning; 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

【Deposit target】ScienceDirect / Mendeley Data
【Data】raw + constructed (or access notes for proprietary)? Y/N
【Code】Dynare/MATLAB/Julia/Stata/R, versions pinned? Y/N
【run_all】regenerates all exhibits incl. IRFs/FEVDs? Y/N
【Seeds + README】present? Y/N
【AI declaration】present? Y/N
【Next step】jme-review-process