pyxtabond2 0.1.1

A Python package for Dynamic Panel Data GMM Estimation, replicating Stata's xtabond2.

PyXtabond2: Dynamic Panel Data Estimation in Python

pyxtabond2 is a comprehensive Python package for estimating dynamic panel data models using the Generalized Method of Moments (GMM). It aims to faithfully replicate the functionality, matrix algebra, and robust diagnostics of Stata's highly popular xtabond2 command developed by David Roodman.

Ideal for applied econometrics and macroeconomic research, this package bridges the gap between Python's data science ecosystem and advanced dynamic panel methodologies.

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🌟 Key Features

• Difference GMM (Arellano-Bond 1991): Standard first-differenced GMM estimation.
• System GMM (Blundell-Bond 1998): Combined level and differenced equations for highly persistent series.
• Interactive Fixed Effects (PCA-GMM): Automatically detect and purge unobserved common factors using Bai & Ng (2002) and Ahn & Horenstein (2013) criteria.
• Windmeijer (2005) Correction: Exact finite-sample correction for two-step GMM standard errors.
• Forward Orthogonal Deviations (FOD): Arellano-Bover (1995) transformation, maximizing sample size in unbalanced panels with gaps.
• Instrument Collapsing: Prevents instrument proliferation and the weakening of overidentification tests.
• Comprehensive Diagnostics: Arellano-Bond AR(1)/AR(2) tests, Sargan/Hansen J-tests, and Difference-in-Hansen tests for instrument exogeneity.
• Direct Export: Export publication-ready tables directly to LaTeX or Microsoft Word.

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📦 Installation

You can install pyxtabond2 directly from PyPI using pip:

📦 Installation

pip install pyxtabond2

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🚀 Quick Start pyxtabond2 comes with integrated example datasets so you can start experimenting immediately.

from pyxtabond2.data_utils import PanelData
from pyxtabond2.api import PyXtabond2
from pyxtabond2.load_data import load_dataset

# 1. Loading the data
# Make sure the df_panel.xlsx file is in the same folder
try:
    df = load_dataset('df_panel.xlsx')
    print(f"Data loaded successfully: {df.shape[0]} observations.")
except FileNotFoundError:
    print("Error: The file 'df_panel.xlsx' could not be found.")

# Data preparation
panel = PanelData(df, id_col='Country', time_col='Year')
panel.data['L1_Growth'] = panel.get_lag('Growth', 1)
df_ready = panel.data.reset_index()

id_col = 'Country'          # Group identifier (country, firm)
time_col = 'Year'           # Time identifier
dep_var = 'Growth'          # Dependent variable
x_vars = ['Capital', 'Labor', 'Wage', 'Investment', 'Ide'] # Explanatory variables
gmm_vars = ['Growth', 'Capital'] # Variables for Arellano-Bond instruments
iv_vars = ['Ide']           # Variables for standard instruments

modele = PyXtabond2(df_ready, 
                    id_col = 'Country', 
                    time_col = 'Year', 
                    dep_var = 'Growth', 
                    x_vars = ['L1_Growth', 'Capital', 'Labor', 'Wage', 'Investment', 'Ide'], 
                    gmm_vars =['Growth', 'Capital'], 
                    iv_vars = ['Ide'],
                    model_type='difference',
                    twostep=False)

result = modele.fit()
result.summary()

# 4. Export results for publication
result.to_latex("gmm_results.tex", full_output=False)
result.to_word("gmm_results.docx", full_output=False)

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📖 References & Methodology This package implements the algorithms and corrections outlined in the following seminal papers:

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies.

Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics.

Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics.

Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics.

Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal.

Bai, J., & Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica.

Ahn, S. C., & Horenstein, A. R. (2013). Eigenvalue ratio test for the number of factors. Econometrica.

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🤝 Contributing Contributions, issues, and feature requests are welcome! Feel free to check the issues page on the GitHub repository.