# Strategic Ambiguity Empirics Pipeline
[](https://github.com/hyunjimoon/empirics_ent_strat_ops/actions/workflows/test.yml)
[](https://codecov.io/gh/hyunjimoon/empirics_ent_strat_ops)
[](https://www.python.org/downloads/)
Empirical analysis pipeline for strategic ambiguity research in venture capital.
## Features
- **Hypothesis Testing**: H1, H2, H3, H4 models with statistical validation
- **Two-Snapshot Validation**: E/L/S trajectory analysis
- **Vagueness Scoring**: Strategic ambiguity measurement (V1, V2)
- **Multiverse Analysis**: Specification curve robustness checks
- **Publication Figures**: F-series visualization generation
## Installation
```bash
# Clone repository
git clone https://github.com/hyunjimoon/empirics_ent_strat_ops.git
cd empirics_ent_strat_ops
# Install dependencies
pip install -r requirements.txt
```
## Running Tests
```bash
# Run all tests
pytest test/unit/test_models.py -v
# Run with coverage
pytest test/unit/test_models.py --cov=src/models
# Run specific test class
pytest test/unit/test_models.py::TestH1EarlyFunding -v
```
## Test Coverage
**Current Status:**
- ✅ `models.py` - 100% coverage (53 tests)
- H1: Early funding ~ vagueness (OLS)
- H2: Growth ~ vagueness × hardware (Logit)
- H3: Log funding ~ vagueness × founder (OLS)
- H4: Growth ~ vagueness × founder (Logit)
- Two-snapshot: E/L/S validation
**Test Suite Features:**
- 15 reusable fixtures with synthetic data
- Edge case handling (missing data, perfect separation)
- Statistical property validation
- Convergence robustness checks
## Pipeline Structure
```
src/
├── models.py # Hypothesis testing (627 lines) ✓ tested
├── features.py # Data loading & feature engineering (1,538 lines)
├── plotting.py # Publication figures (1,012 lines)
├── cache_manager.py # Pipeline caching (506 lines)
├── cli.py # Command-line interface (898 lines)
└── vagueness_v2.py # Strategic vagueness scorer (647 lines)
test/
├── conftest.py # Shared fixtures
└── unit/
└── test_models.py # Hypothesis testing tests (53 tests)
```
## Usage
### Data Analysis Pipeline
```bash
# Load and engineer features
python -m src.cli load-data
python -m src.cli engineer-features
# Run hypothesis tests
python -m src.cli run-models --dataset quantum
# Generate figures
python -m src.cli generate-plots --dataset quantum
```
### Paper Generation Pipeline (NEW!)
**Complete pipeline** (Data → Analysis → Paper PDF):
```bash
make all
```
**Quick rebuild** (skip data processing):
```bash
make quick
```
**Individual steps**:
```bash
# Step 1: Process data
make data
# Step 2: Run statistical analyses and generate Results section
make analysis
# Step 3: Generate tables
make tables
# Step 4: Generate figures
make figures
# Step 5: Compile LaTeX to PDF
make paper
```
**Key outputs**:
- `paper/results_auto.tex` - Auto-generated Results section (Module #23-27)
- `paper/tables/*.tex` - LaTeX tables (Table 1-2)
- `paper/figures/*.pdf` - All figures (Fig 2-3)
- `paper/output/main.pdf` - Final paper PDF
See [`docs/PAPER_PIPELINE_GUIDE.md`](docs/PAPER_PIPELINE_GUIDE.md) for detailed documentation.
## Contributing
1. Fork the repository
2. Create a feature branch
3. Run tests: `pytest test/unit/ -v`
4. Submit a pull request
## Citation
If you use this code in your research, please cite:
```bibtex
@software{strategic_ambiguity_empirics,
title = {Strategic Ambiguity Empirics Pipeline},
author = {Moon, Hyunji},
year = {2024},
url = {https://github.com/hyunjimoon/empirics_ent_strat_ops}
}
```
## License
MIT License