Jfqa Replication And Data Policy

JFQA Replication & Data Policy (jfqa-replication-and-data-policy)

Use this skill to satisfy the JFQA Code Sharing Policy. It is mandatory for submissions made on or after January 1, 2024 if the paper is accepted (voluntary for earlier submissions). Materials are posted at acceptance, before online publication. Re-verify the live policy before final packaging.

What you must deposit

1. Source code that reproduces all reported findings from the raw data — every table and figure.
2. The raw datasets, OR — if restricted by copyright/confidentiality (common with CRSP, Compustat, TAQ, etc.) — a pseudo dataset with enough observations that all programs execute successfully.
3. A "read me" file documenting the software, languages, and data formats, plus a roadmap of the program execution order when there are multiple programs.

Where it goes

• Archived in the JFQA Dataverse, hosted at the Harvard University Dataverse, as supplemental materials.
• Code is licensed for academic research only; users must acknowledge the code's origin.

Exceptions (timing matters)

• Any exception — e.g., delayed code sharing — must be requested on the initial submission, not later. The handling editor decides; an approved exception is noted in the published paper.

Verification

• JFQA may use external verification services to validate code for randomly selected publications. Build the package so a third party can run it end-to-end from the (raw or pseudo) data.

Build discipline

• One master script (run_all) regenerating every exhibit from the deposited data.
• Pin software/package versions; set and report seeds for any bootstrap/simulation.
• Keep paths relative; ensure the pseudo dataset triggers every code path.

Pseudo-data recipe for licensed finance sources

CRSP, Compustat, TAQ, IBES, and OptionMetrics extracts cannot be redistributed, so the pseudo dataset carries the verification load:

1. Preserve the exact schema — variable names, types, and panel keys (permno/gvkey/date) — so merges run unchanged.
2. Simulate or scramble enough rows (say, 500 firms over 120 months; scale to your design) to exercise every merge, filter, and edge case: missing delisting returns, duplicate links, zero-volume days, fiscal-year changes.
3. Run the full pipeline on the pseudo data and confirm every program completes and every exhibit is produced in well-formed (not numerically identical) form.
4. In the read-me, state plainly that pseudo-data numbers will not match the paper, and specify the exact licensed extracts a verifier needs (data vendor, library/table names, variable list, query date range) to reproduce the real ones.

Archive layout and dry-run protocol

jfqa-archive/
  README.md            # software + versions, data inventory, execution roadmap
  run_all.sh           # one-button rebuild of every table and figure
  code/                # numbered: 01_build_sample, 02_main_tables, ...
  data/raw/  or  data/pseudo/
  output/tables/  output/figures/

• Fresh-machine test: copy the archive to a clean directory (ideally a colleague's machine), run run_all, and diff the regenerated exhibits against the manuscript.
• Confirm the read-me reflects the academic-research-only license and the requirement that users acknowledge the code's origin.
• Because verification may be performed by an external service on randomly selected papers, write the read-me for a stranger with no context, not for your coauthors.

Exception decision aid

Output format

【Code】reproduces all tables/figures from raw data? [Y/N]
【Data】raw OR pseudo dataset that runs end-to-end? [which]
【Read-me】software/versions + execution roadmap? [Y/N]
【Deposit】JFQA Dataverse (Harvard), academic-use license? [Y/N]
【Exception】needed? if so, requested at INITIAL submission? [Y/N/NA]
【Next step】jfqa-submission