# RegHDFE
**Note: This package continues to be maintained. Additionally, `reghdfe` functionality is also integrated into [StatsPAI](https://github.com/brycewang-stanford/StatsPAI/) for users who prefer the unified ecosystem.**
---
[](https://pypi.org/project/reghdfe/)
[](https://pypi.org/project/reghdfe/)
[](https://opensource.org/licenses/MIT)
[](https://pypi.org/project/reghdfe/)
Python implementation of Stata's reghdfe for high-dimensional fixed effects regression.
## Installation
```bash
pip install reghdfe
```
## 📖 Quick Start
### Basic Example
```python
import pandas as pd
import numpy as np
from reghdfe import reghdfe
# Create sample data
np.random.seed(42)
n = 1000
data = pd.DataFrame({
'wage': np.random.normal(10, 2, n),
'experience': np.random.normal(5, 2, n),
'education': np.random.normal(12, 3, n),
'firm_id': np.random.choice(range(100), n),
'year': np.random.choice(range(2010, 2020), n)
})
# Run regression with firm fixed effects
result = reghdfe(
data=data,
y='wage',
x=['experience', 'education'],
fe=['firm_id']
)
# Display results
print(result.summary())
```
### Advanced Usage Examples
#### 1. Multiple Fixed Effects
```python
# Regression with firm and year fixed effects
result = reghdfe(
data=data,
y='wage',
x=['experience', 'education'],
fe=['firm_id', 'year'] # Multiple dimensions
)
print(result.summary())
```
#### 2. Cluster-Robust Standard Errors
```python
# One-way clustering
result = reghdfe(
data=data,
y='wage',
x=['experience', 'education'],
fe=['firm_id'],
cluster=['firm_id'] # Cluster by firm
)
# Two-way clustering
result = reghdfe(
data=data,
y='wage',
x=['experience', 'education'],
fe=['firm_id'],
cluster=['firm_id', 'year'] # Cluster by firm and year
)
```
#### 3. Weighted Regression
```python
# Add weights to your data
data['weight'] = np.random.uniform(0.5, 2.0, len(data))
# Run weighted regression
result = reghdfe(
data=data,
y='wage',
x=['experience', 'education'],
fe=['firm_id'],
weights='weight'
)
```
#### 4. No Fixed Effects (OLS)
```python
# Simple OLS regression
result = reghdfe(
data=data,
y='wage',
x=['experience', 'education'],
fe=None # No fixed effects
)
```
## Working with Results
### Accessing Coefficients and Statistics
```python
result = reghdfe(data=data, y='wage', x=['experience', 'education'], fe=['firm_id'])
# Get coefficients
coefficients = result.coef
print("Coefficients:", coefficients)
# Get standard errors
std_errors = result.se
print("Standard Errors:", std_errors)
# Get t-statistics
t_stats = result.tstat
print("T-statistics:", t_stats)
# Get p-values
p_values = result.pvalue
print("P-values:", p_values)
# Get confidence intervals
conf_int = result.conf_int()
print("95% Confidence Intervals:", conf_int)
# Get R-squared
print(f"R-squared: {result.rsquared:.4f}")
print(f"Adjusted R-squared: {result.rsquared_adj:.4f}")
```
### Summary Statistics
```python
# Full regression summary
print(result.summary())
# Detailed summary with additional statistics
print(result.summary(show_dof=True))
```
## 🔧 Advanced Configuration
### Custom Absorption Options
```python
result = reghdfe(
data=data,
y='wage',
x=['experience', 'education'],
fe=['firm_id'],
absorb_tolerance=1e-10, # Higher precision
drop_singletons=True, # Drop singleton groups
absorb_method='lsmr' # Alternative solver
)
```
### Different Covariance Types
```python
# Robust standard errors (default)
result = reghdfe(data=data, y='wage', x=['experience'], fe=['firm_id'],
cov_type='robust')
# Clustered standard errors
result = reghdfe(data=data, y='wage', x=['experience'], fe=['firm_id'],
cov_type='cluster', cluster=['firm_id'])
```
## Comparison with Stata
This package aims to replicate Stata's `reghdfe` command. Here's how the syntax translates:
**Stata:**
```stata
reghdfe wage experience education, absorb(firm_id year) cluster(firm_id)
```
**Python (reghdfe):**
```python
result = reghdfe(
data=data,
y='wage',
x=['experience', 'education'],
fe=['firm_id', 'year'],
cluster=['firm_id']
)
```
## 📋 Key Features
- ✅ **High-dimensional fixed effects** - Efficiently absorb multiple fixed effect dimensions
- ✅ **Cluster-robust standard errors** - Support for one-way and two-way clustering
- ✅ **Weighted regression** - Handle sampling weights and frequency weights
- ✅ **Singleton dropping** - Automatically handle singleton groups
- ✅ **Fast computation** - Optimized algorithms for large datasets
- ✅ **Stata compatibility** - Results match Stata's reghdfe command
## Integration Options
This package is **actively maintained** as a standalone library. For users who prefer a unified ecosystem with additional econometric and statistical tools, `reghdfe` functionality is also available through:
- **[StatsPAI](https://github.com/brycewang-stanford/StatsPAI/)** - Stats + Econometrics + ML + AI + LLMs
## Related Projects
- **[StatsPAI](https://github.com/brycewang-stanford/StatsPAI/)** - StatsPAI = Stats + Econometrics + ML + AI + LLMs
- **[PyStataR](https://github.com/brycewang-stanford/PyStataR)** - Unified Stata-equivalent commands and R functions
## Documentation
For detailed API reference and additional examples, visit our [GitHub repository](https://github.com/brycewang-stanford/reghdfe).
## Contributing
We welcome contributions! Please feel free to:
- Report bugs or request features via [GitHub Issues](https://github.com/brycewang-stanford/reghdfe/issues)
- Submit pull requests for improvements
- Share your use cases and examples
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
**This package is actively maintained.** For questions, bug reports, or feature requests, please open an issue on [GitHub](https://github.com/brycewang-stanford/reghdfe/issues).
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"description": "# RegHDFE\n\n**Note: This package continues to be maintained. Additionally, `reghdfe` functionality is also integrated into [StatsPAI](https://github.com/brycewang-stanford/StatsPAI/) for users who prefer the unified ecosystem.**\n\n---\n\n[](https://pypi.org/project/reghdfe/)\n[](https://pypi.org/project/reghdfe/)\n[](https://opensource.org/licenses/MIT)\n[](https://pypi.org/project/reghdfe/)\n\nPython implementation of Stata's reghdfe for high-dimensional fixed effects regression.\n\n## Installation\n\n```bash\npip install reghdfe\n```\n\n## \ud83d\udcd6 Quick Start\n\n### Basic Example\n\n```python\nimport pandas as pd\nimport numpy as np\nfrom reghdfe import reghdfe\n\n# Create sample data\nnp.random.seed(42)\nn = 1000\ndata = pd.DataFrame({\n 'wage': np.random.normal(10, 2, n),\n 'experience': np.random.normal(5, 2, n),\n 'education': np.random.normal(12, 3, n),\n 'firm_id': np.random.choice(range(100), n),\n 'year': np.random.choice(range(2010, 2020), n)\n})\n\n# Run regression with firm fixed effects\nresult = reghdfe(\n data=data,\n y='wage',\n x=['experience', 'education'],\n fe=['firm_id']\n)\n\n# Display results\nprint(result.summary())\n```\n\n### Advanced Usage Examples\n\n#### 1. Multiple Fixed Effects\n\n```python\n# Regression with firm and year fixed effects\nresult = reghdfe(\n data=data,\n y='wage',\n x=['experience', 'education'],\n fe=['firm_id', 'year'] # Multiple dimensions\n)\nprint(result.summary())\n```\n\n#### 2. Cluster-Robust Standard Errors\n\n```python\n# One-way clustering\nresult = reghdfe(\n data=data,\n y='wage',\n x=['experience', 'education'],\n fe=['firm_id'],\n cluster=['firm_id'] # Cluster by firm\n)\n\n# Two-way clustering\nresult = reghdfe(\n data=data,\n y='wage',\n x=['experience', 'education'],\n fe=['firm_id'],\n cluster=['firm_id', 'year'] # Cluster by firm and year\n)\n```\n\n#### 3. Weighted Regression\n\n```python\n# Add weights to your data\ndata['weight'] = np.random.uniform(0.5, 2.0, len(data))\n\n# Run weighted regression\nresult = reghdfe(\n data=data,\n y='wage',\n x=['experience', 'education'],\n fe=['firm_id'],\n weights='weight'\n)\n```\n\n#### 4. No Fixed Effects (OLS)\n\n```python\n# Simple OLS regression\nresult = reghdfe(\n data=data,\n y='wage',\n x=['experience', 'education'],\n fe=None # No fixed effects\n)\n```\n\n## Working with Results\n\n### Accessing Coefficients and Statistics\n\n```python\nresult = reghdfe(data=data, y='wage', x=['experience', 'education'], fe=['firm_id'])\n\n# Get coefficients\ncoefficients = result.coef\nprint(\"Coefficients:\", coefficients)\n\n# Get standard errors\nstd_errors = result.se\nprint(\"Standard Errors:\", std_errors)\n\n# Get t-statistics\nt_stats = result.tstat\nprint(\"T-statistics:\", t_stats)\n\n# Get p-values\np_values = result.pvalue\nprint(\"P-values:\", p_values)\n\n# Get confidence intervals\nconf_int = result.conf_int()\nprint(\"95% Confidence Intervals:\", conf_int)\n\n# Get R-squared\nprint(f\"R-squared: {result.rsquared:.4f}\")\nprint(f\"Adjusted R-squared: {result.rsquared_adj:.4f}\")\n```\n\n### Summary Statistics\n\n```python\n# Full regression summary\nprint(result.summary())\n\n# Detailed summary with additional statistics\nprint(result.summary(show_dof=True))\n```\n\n## \ud83d\udd27 Advanced Configuration\n\n### Custom Absorption Options\n\n```python\nresult = reghdfe(\n data=data,\n y='wage',\n x=['experience', 'education'],\n fe=['firm_id'],\n absorb_tolerance=1e-10, # Higher precision\n drop_singletons=True, # Drop singleton groups\n absorb_method='lsmr' # Alternative solver\n)\n```\n\n### Different Covariance Types\n\n```python\n# Robust standard errors (default)\nresult = reghdfe(data=data, y='wage', x=['experience'], fe=['firm_id'], \n cov_type='robust')\n\n# Clustered standard errors\nresult = reghdfe(data=data, y='wage', x=['experience'], fe=['firm_id'], \n cov_type='cluster', cluster=['firm_id'])\n```\n\n## Comparison with Stata\n\nThis package aims to replicate Stata's `reghdfe` command. Here's how the syntax translates:\n\n**Stata:**\n```stata\nreghdfe wage experience education, absorb(firm_id year) cluster(firm_id)\n```\n\n**Python (reghdfe):**\n```python\nresult = reghdfe(\n data=data,\n y='wage',\n x=['experience', 'education'],\n fe=['firm_id', 'year'],\n cluster=['firm_id']\n)\n```\n\n## \ud83d\udccb Key Features\n\n- \u2705 **High-dimensional fixed effects** - Efficiently absorb multiple fixed effect dimensions\n- \u2705 **Cluster-robust standard errors** - Support for one-way and two-way clustering \n- \u2705 **Weighted regression** - Handle sampling weights and frequency weights\n- \u2705 **Singleton dropping** - Automatically handle singleton groups\n- \u2705 **Fast computation** - Optimized algorithms for large datasets\n- \u2705 **Stata compatibility** - Results match Stata's reghdfe command\n\n## Integration Options\n\nThis package is **actively maintained** as a standalone library. For users who prefer a unified ecosystem with additional econometric and statistical tools, `reghdfe` functionality is also available through:\n\n- **[StatsPAI](https://github.com/brycewang-stanford/StatsPAI/)** - Stats + Econometrics + ML + AI + LLMs\n\n## Related Projects\n\n- **[StatsPAI](https://github.com/brycewang-stanford/StatsPAI/)** - StatsPAI = Stats + Econometrics + ML + AI + LLMs \n- **[PyStataR](https://github.com/brycewang-stanford/PyStataR)** - Unified Stata-equivalent commands and R functions\n\n## Documentation\n\nFor detailed API reference and additional examples, visit our [GitHub repository](https://github.com/brycewang-stanford/reghdfe).\n\n## Contributing\n\nWe welcome contributions! Please feel free to:\n- Report bugs or request features via [GitHub Issues](https://github.com/brycewang-stanford/reghdfe/issues)\n- Submit pull requests for improvements\n- Share your use cases and examples\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n\n**This package is actively maintained.** For questions, bug reports, or feature requests, please open an issue on [GitHub](https://github.com/brycewang-stanford/reghdfe/issues).\n",
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