Aejmac Replication Package

Replication Package (aejmac-replication-package)

When to trigger

• A paper is heading toward conditional acceptance and the AEA Data Editor check is next
• You have simulation/calibration code but have never packaged it for a reviewer to run
• The data include a restricted/proprietary source (confidential micro data, licensed series)
• You want to build the package as you go rather than scrambling at acceptance

The AEA reproducibility regime (verified 2026-06; re-confirm on the official AEA pages)

• Governed by the AEA Data and Code Availability Policy. Conditionally accepted papers undergo a review by the AEA Data Editor (Lars Vilhuber) before publication, including reproducibility checks and verification of the information provided.
• Deposit in the AEA Data and Code Repository on openICPSR (use is strongly encouraged; other trusted repositories may be allowed with Data Editor approval). Materials are posted with the article.
• Code scope is broad — and this is the macro-critical point: the policy covers data cleaning and "estimation, simulation, model solution, and visualization" code. For AEJ: Macro, the DSGE/HANK solver, the calibration/estimation routines, and the simulation code must all be in the package, not only the regression scripts.
• Restricted-access data: exceptions exist for confidential / copyrighted / agreement-restricted data; authors must preserve materials 5+ years, provide reasonable replication assistance, make the code public even when the data cannot be, and disclose data sources. State any such request to the Data Editor.
• Field experiments must be registered in the AEA RCT Registry.

Building a macro-grade package

Directory & master script

• A clear tree: /data (raw + analysis), /code, /output (tables + figures), /docs.
• One master script (run_all) that regenerates every table and figure from raw inputs in order, including the model solution and simulation steps.
• A README following the AEA template: data sources and access, software + versions, hardware, expected runtime, and a map from each exhibit to the script that makes it.

Macro-specific reproducibility

• Pin the toolchain: Stata version + ssc/net package versions; R renv.lock; Python requirements.txt/conda env; Julia Project.toml/Manifest.toml; Dynare version for DSGE.
• Seeds set and reported for every simulation, bootstrap, and randomization step.
• Long-running computations (global solutions, large HANK simulations, MCMC): provide a way to verify without a supercomputer — ship intermediate/cached outputs and a reduced-scale switch, and document expected full runtime.
• Numerical accuracy artifacts: include the diagnostics (Euler errors, grid checks) so the Data Editor can confirm the solution, not just rerun it.

Data documentation

• For each source: provider, exact extract/vintage, access date, license, and whether it is public or restricted.
• Real-time vs. revised macro vintages (e.g., ALFRED): document which you used.
• Restricted data: a clear access path and a public code subset that runs on synthetic/sample data where possible.

Checklist

• openICPSR (AEA Data and Code Repository) deposit planned; README on the AEA template
• Master run_all regenerates every exhibit incl. model solution + simulation
• Simulation, calibration/estimation, and solver code all included (not just regressions)
• Toolchain pinned (Stata/R/Python/Julia/Dynare versions); seeds set and reported
• Long-running steps: cached outputs + reduced-scale switch + runtime documented
• Restricted data: exception request stated; code public; access path documented; 5-year retention noted
• Every data source documented (provider, vintage, access date, license)
• Field experiments registered in the AEA RCT Registry

Anti-patterns

• Packaging only the regression scripts and omitting the DSGE solver / simulation code
• "Results available on request" instead of a deposited, runnable package
• Unpinned package versions, so the Data Editor cannot reproduce the numbers
• Unseeded simulations that do not reproduce
• A multi-day computation with no reduced-scale path or cached intermediates
• Discovering a data-license problem at acceptance instead of flagging it early

Output format

【Repository】openICPSR (AEA Data and Code Repository) deposit ready? [Y/N]
【Master script】run_all regenerates all exhibits incl. model+simulation? [Y/N]
【Code scope】solver + calibration/estimation + simulation + cleaning all included? [Y/N]
【Toolchain + seeds】versions pinned; seeds reported? [Y/N]
【Restricted data】exception stated; code public; access path documented? [Y/N / NA]
【Long runs】cached outputs + reduced-scale switch + runtime noted? [Y/N / NA]
【Next step】aejmac-referee-strategy

Source: brycewang-stanford/Awesome-Journal-Skills → AEJ-Macroeconomics-Skills/skills/aejmac-replication-package/SKILL.md