reghdfe 0.1.1

Python implementation of Stata's reghdfe for high-dimensional fixed effects regression

RegHDFE

Note: This package continues to be maintained. Additionally, reghdfe functionality is also integrated into StatsPAI for users who prefer the unified ecosystem.

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Python implementation of Stata's reghdfe for high-dimensional fixed effects regression.

Installation

pip install reghdfe

📖 Quick Start

Basic Example

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

# 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

# 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

# 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)

# Simple OLS regression
result = reghdfe(
    data=data,
    y='wage',
    x=['experience', 'education'],
    fe=None  # No fixed effects
)

Working with Results

Accessing Coefficients and Statistics

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

# Full regression summary
print(result.summary())

# Detailed summary with additional statistics
print(result.summary(show_dof=True))

🔧 Advanced Configuration

Custom Absorption Options

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

# 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:

reghdfe wage experience education, absorb(firm_id year) cluster(firm_id)

Python (reghdfe):

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 - Stats + Econometrics + ML + AI + LLMs

Related Projects

• StatsPAI - StatsPAI = Stats + Econometrics + ML + AI + LLMs
• PyStataR - Unified Stata-equivalent commands and R functions

Documentation

For detailed API reference and additional examples, visit our GitHub repository.

Contributing

We welcome contributions! Please feel free to:

• Report bugs or request features via GitHub Issues
• Submit pull requests for improvements
• Share your use cases and examples

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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This package is actively maintained. For questions, bug reports, or feature requests, please open an issue on GitHub.