sadiqmuhd/Nigeria-Crude-Oil-Export-Intelligence-Analysis

End-to-end quantitative analysis of Nigeria's crude oil export trends (2010–2024), producing a structured trade intelligence report with policy implications for intra-African commodity trade under the AfCFTA framework. # Nigeria Crude Oil Export Intelligence Analysis > **End-to-end quantitative analysis of Nigeria's crude oil export trends (2010–2024) — combining regression modelling, GARCH volatility analysis, and trade finance gap estimation to produce structured policy intelligence for intra-African commodity trade under the AfCFTA framework.** -2ECC71?style=flat) --- ## Table of Contents - Project Overview - Key Findings & Inferences - Visual Results - Analytical Architecture - Project Structure - Quick Start - Data Sources - Methodology - Policy Implications - Limitations & Extensions - Author --- ## Project Overview Nigeria's crude oil sector accounts for **over 80% of merchandise export revenue** yet remains structurally exposed to three compounding vulnerabilities: global price shocks, foreign exchange amplification, and near-zero intra-African market penetration. This project builds a **fully reproducible, five-layer analytical pipeline** that quantifies these risks and produces intelligence outputs replicating the methodology of multilateral trade finance institutions (AfDB, IFC, Afreximbank, WTO). ### What this project does | Layer | Component | Output | |-------|-----------|--------| | **1** | Data Ingestion | Raw data from UN Comtrade API, CBN/World Bank FX, OPEC ASB | | **2** | Data Processing | Master panel (15 years × 20 engineered features) | | **3** | Module A — Trend Analysis | LOWESS decomposition, HHI, partner shifts, Africa share | | **4** | Module B — Regression | OLS elasticity, Granger causality, VAR impulse response | | **4** | Module C — Volatility | GARCH(1,1), shock identification, trade finance gap | | **5** | Outputs | 8 figures, 15 CSV tables, full intelligence report | Run everything with a single command: ```bash python run_pipeline.py ``` --- ## Key Findings & Inferences ### 1. Brent Price Explains 89.5% of Nigeria's Export Revenue Variance A log-log OLS regression on 15 years of annual data produces a **price elasticity …