Jme Replication And Data Policy

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.

What monetary-macro referees actually re-run

Build the package around the objects a JME referee is most likely to reconstruct, not around generic tidiness:

• The identified shock series itself. If identification rests on high-frequency policy surprises or narrative shocks, deposit the series and its construction code. Referees swap in alternative shock measures and re-trace the IRFs; a package that ships only the final impulse responses invites a hostile report.
• DSGE posterior machinery. Include priors, the posterior mode, chain settings, and convergence diagnostics — and pin the Dynare release, because steady-state solvers and default options shift across versions and can move estimated parameters.
• Real-time vintages. For Taylor-rule, forecasting, or policy-reaction work, archive the exact data vintage used; revised series change coefficients, and "latest vintage" is not a reproducible input.
• Moment-matching map. For calibrated or estimated models, ship one script that prints the data moments next to the model moments in the paper's Table order, so the fit claim is checkable in minutes.

An editor deciding whether to require materials as a condition of publication reads this layer as the difference between a deposit and an afterthought.

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

Source: brycewang-stanford/Awesome-Journal-Skills → Journal-of-Monetary-Economics-Skills/skills/jme-replication-and-data-policy/SKILL.md