⏳ PENDING VERIFICATION  ·  This firm's basic facts (CIK, dates, mechanism) are still being verified against primary SEC sources before active Tier-3 audit dispatch. Data may be incomplete or subject to revision.  audit framework

Replication · open kit, three platforms, tolerance-based diff

Anyone can verify every number on this subsite.

Bottom line. The Zion Oil & Gas replication kit ships the canonical Python pipeline (single source of truth for every number on the subsite); R (eventstudies) and Stata cross-check scripts; tolerance-based diff against expected_results.json. Tolerances are universal across the SMU CGI Reincorporation Tracker: ±0.5pp on point estimates, ±0.05 on p-values, ±0.01 on R², ±0.05 on donor weights. Kit status is pending per-firm event-study run; the file scaffolding ships with this build.

Read this first — what each file is for (when kit lands)
  • Daily-close panel — adjusted close prices for Zion Oil & Gas and the oil and gas exploration (Israel-focused operations) donor pool (Ring Energy, Granite Ridge Resources, Riley Exploration Permian, Vital Energy, Crescent Energy, SilverBow Resources, etc.), T−260 through T+10 around canonical event date. [KIT PENDING]
  • Donor weights — SLSQP-simplex optimization output; sum-to-1 at nine-decimal precision. [KIT PENDING]
  • Reference answers — expected_results.json for tolerance-based diff. [KIT PENDING]
  • Three pipeline scripts — Python (canonical), R, Stata. [KIT PENDING]
  • Walk-through README — installation, expected output, tolerance-diff checking. [KIT PENDING]
  • Data-derivation guide — rebuild the price panel from S&P Capital IQ, CRSP via WRDS, Ken French factor library. Inherits from canonical HOW_TO_PULL_DATA.md. [KIT PENDING]

Cross-platform verification tolerances

±0.5 pp
Point-estimate tolerance
±0.05
P-value tolerance
±0.01
R² tolerance
±0.05
Donor-weight tolerance

How to replicate (under 10 minutes, any one language)

  1. Download the kit from this folder once available. Files target ~ <500 KB total (excluding article PDF).
  2. Pick a language. Python is the canonical pipeline; R and Stata cross-checks ship in the same kit.
    • Python: pip install pandas numpy statsmodels scipy, then python event_study.py
    • R: install jsonlite, then Rscript event_study.R
    • Stata: open Stata, change directory to the kit folder, run do event_study.do
  3. Each script writes results.json and prints a tolerance-based diff against expected_results.json. Zero out-of-tolerance flags = successful replication.

Where the data comes from

  • Zion Oil & Gas and auto-peer daily prices: S&P Capital IQ IQ_CLOSEPRICE_ADJ feed. Cross-check via CRSP (WRDS, academic-grade).
  • Factor returns: Ken French Data Library (FF5 + UMD).
  • Oil benchmark (sensitivity only): United States Brent Oil Fund (BNO) daily returns; WTI spot and Brent futures alternatives in the data-derivation guide.
  • 13F ownership snapshot: S&P Capital IQ Public Ownership Detailed, pre-event-date snapshot.
  • SEC filings: direct EDGAR accession URLs for every PRE 14A, DEF 14A, DEFA14A, 8-K cited.

Canonical primary sources

SEC EDGAR — Zion Oil & Gas

  • Zion Oil & Gas DEF 14A (April 29, 2024) — primary source verification pending (prior link removed: fake accession from build template; see canonical data.json for the firm's real event accession).
  • Zion Oil & Gas 8-K Item 5.07 (June 14, 2024) — primary source verification pending (prior link removed: fake accession from build template; see canonical data.json for the firm's real event accession).
  • Zion Oil & Gas 8-K (cover-page state change, 2025-04-09) — primary source verification pending (prior link removed: fake accession from build template; see canonical data.json for the firm's real event accession).

Daily closes, donor weights, expected results, and the Python / R / Stata scripts download directly from this page when the kit lands. The README walks through installation, expected output, and tolerance-based diff checking. Questions, corrections, or substantive discrepancies are welcome — please email Shane Goodwin (sgoodwin@smu.edu) at SMU Cox / SMU Dedman.