panelbox 1.0.1

Panel data econometrics in Python: Fixed Effects, Random Effects, GMM (Arellano-Bond, Blundell-Bond), Experiment Pattern with Result Containers (Validation, Comparison, Residual), Test Runners & Master Reports, Interactive Visualizations (35 Charts), Professional HTML Reports, Robust Standard Errors (HC, Clustered, Driscoll-Kraay, Newey-West), Comprehensive Diagnostics

PanelBox

Panel Data Econometrics in Python

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PanelBox is a comprehensive Python library for panel data econometrics, with 70+ models across 13 families, 50+ diagnostic tests, and 35+ interactive charts. It brings the capabilities of Stata's xtabond2, xtreg, xtfrontier, and R's plm, splm, frontier to Python with a modern, unified API.

Installation

pip install panelbox

Quick Start

import panelbox as pb

# Load bundled dataset (103 datasets available)
data = pb.datasets.load_grunfeld()

# Fixed Effects model
fe = pb.FixedEffects(
    formula="invest ~ value + capital",
    data=data,
    entity_col="firm",
    time_col="year"
)
results = fe.fit(cov_type='clustered')
print(results.summary())

Model Families

Static Panel Models

Model	Description
PooledOLS	Pooled OLS estimation
FixedEffects	Within estimator (entity/time/two-way)
RandomEffects	GLS estimation
BetweenEstimator	Between-groups estimator
FirstDifferenceEstimator	First-difference estimator

Dynamic Panel GMM

Model	Description
DifferenceGMM	Arellano-Bond (1991)
SystemGMM	Blundell-Bond (1998)
ContinuousUpdatedGMM	CUE-GMM (Hansen-Heaton-Yaron 1996)
BiasCorrectedGMM	Hahn-Kuersteiner (2002) bias correction

Full diagnostic suite: Hansen J, Sargan, AR(1)/AR(2), Windmeijer correction, instrument ratio monitoring, overfit diagnostics.

Panel VAR

Model	Description
PanelVAR	Panel Vector Autoregression (OLS/GMM)
PanelVECM	Panel Vector Error Correction Model

Includes IRF, FEVD, Granger causality network visualization, lag selection (AIC/BIC/HQIC), and Johansen cointegration rank test.

Spatial Models

Model	Description
SpatialLag	Spatial Autoregressive Model (SAR)
SpatialError	Spatial Error Model (SEM)
SpatialDurbin	Spatial Durbin Model (SDM)
GeneralNestingSpatial	General Nesting Spatial (GNS)
DynamicSpatialPanel	Dynamic spatial panel models

Stochastic Frontier Analysis

Model	Description
StochasticFrontier	SFA with half-normal, exponential, truncated-normal, gamma
FourComponentSFA	Persistent/transient inefficiency decomposition

JLMS, BC, and Mode efficiency estimators. TFP decomposition and frontier visualization.

Count Data Models

Model	Description
PoissonFixedEffects	Conditional MLE (Hausman-Hall-Griliches 1984)
RandomEffectsPoisson	RE Poisson (Gamma/Normal mixing)
NegativeBinomial	NB2 for overdispersion
ZeroInflatedPoisson	ZIP model
ZeroInflatedNegativeBinomial	ZINB model
PPML	Poisson Pseudo-ML (gravity models)

Discrete Choice Models

Model	Description
FixedEffectsLogit	Conditional logit (Chamberlain 1980)
RandomEffectsProbit	RE probit with GHQ integration
OrderedLogit / OrderedProbit	Ordered choice models
MultinomialLogit	Multinomial choice (FE/RE/Pooled)

Quantile Regression

Model	Description
FixedEffectsQuantile	Koenker (2004) FE quantile regression
CanayTwoStep	Canay (2011) two-step estimator
LocationScale	MSS (2019) location-scale models
DynamicQuantile	Dynamic panel quantile
QuantileTreatmentEffects	Quantile treatment effects

Selection & Censored Models

Model	Description
PanelHeckman	Two-step Heckman (Wooldridge 1995) and MLE
PanelIV	Panel IV/2SLS estimation

Diagnostic Tests (50+)

Category	Tests
Unit Root	LLC, IPS, Fisher, Hadri, Breitung
Cointegration	Kao, Pedroni (7 stats), Westerlund (4 stats)
Specification	Hausman, Mundlak, RESET, Chow, Davidson-MacKinnon J/Cox
Heteroskedasticity	Breusch-Pagan, White, Modified Wald
Serial Correlation	Wooldridge AR, Breusch-Godfrey, Baltagi-Wu
Cross-Sectional Dependence	Pesaran CD, Frees, Breusch-Pagan LM
Spatial	LM Lag/Error (standard + robust), Moran's I, Local LISA
GMM	Hansen J, Sargan, AR(1)/AR(2), weak instruments
Frontier	LR, Wald, skewness, Vuong, inefficiency presence

Robust Standard Errors (8 types)

• HC0-HC3: Heteroskedasticity-consistent (White, leverage-adjusted)
• Clustered: One-way (entity/time) and two-way (Cameron-Gelbach-Miller 2011)
• Driscoll-Kraay: Spatial and temporal dependence
• Newey-West: HAC for serial correlation
• PCSE: Panel-corrected (Beck-Katz 1995)
• Spatial HAC: For spatial panel models

Visualization (35+ interactive charts)

from panelbox.visualization import (
    create_residual_diagnostics,
    create_validation_charts,
    create_comparison_charts,
    create_panel_charts,
    export_charts,
)

# Residual diagnostics (Q-Q, fitted vs residual, scale-location, etc.)
charts = create_residual_diagnostics(results)
export_charts(charts, "diagnostics.html")

# Entity/time effects, between-within decomposition, panel structure
charts = create_panel_charts(results)

Three professional themes: professional, academic, presentation. Export to HTML, JSON, PNG, SVG, PDF.

Experiment Pattern

The PanelExperiment class provides a factory-based workflow for comparing models:

import panelbox as pb

data = pb.datasets.load_grunfeld()

experiment = pb.PanelExperiment(
    data=data,
    formula="invest ~ value + capital",
    entity_col="firm",
    time_col="year"
)

# Fit and compare models
experiment.fit_all_models(names=['pooled', 'fe', 're'])
comparison = experiment.compare_models(['pooled', 'fe', 're'])
print(f"Best model: {comparison.best_model}")

# Validate specification
validation = experiment.validate_model('fe')
validation.save_html('validation.html', test_type='validation')

# Residual diagnostics
residuals = experiment.analyze_residuals('fe')
print(residuals.summary())

# Master report linking all sub-reports
experiment.save_master_report('report.html', theme='professional', reports=[...])

AutoExperiment (AutoML for Panel Data)

AutoExperiment automates the entire panel data modeling pipeline — from variable transformation and selection to multi-model estimation, econometric validation, and ranking:

from panelbox.autoexperiment import AutoExperiment

auto = AutoExperiment(
    data=data,
    depvar="invest",
    entity_col="firm",
    time_col="year",
    sign_constraints={"value": "+", "capital": "+"},
)
results = auto.run()
print(results.summary())
results.report("report.html")

• Variable transformations: lags, diffs, logs, growth rates, rolling means, squares
• Forward stepwise selection by BIC/AIC with sign constraints from economic theory
• Multi-model comparison: Pooled OLS, Fixed Effects, Random Effects, First Difference
• Automatic validation: Hausman, Pesaran CD, RESET, Modified Wald, and more
• Auto-selected standard errors based on diagnostic test results
• Composite ranking combining fit, diagnostics, theory compliance, and parsimony
• Data mining warnings when too many combinations are tested

Bundled Datasets (103 datasets)

from panelbox.datasets import load_dataset, list_datasets, list_categories

# Browse categories
print(list_categories())
# ['censored', 'count', 'diagnostics', 'discrete', 'frontier', 'gmm',
#  'marginal_effects', 'production', 'quantile', 'spatial', 'standard_errors',
#  'validation', 'var']

data = load_dataset("healthcare_visits")
grunfeld = load_dataset("grunfeld")

All 80+ example notebooks use load_dataset() and work directly in Google Colab.

Comparison with Other Packages

Feature	PanelBox	linearmodels	pyfixest	splm (R)
Static panel (FE/RE)	5 models	5 models	2 models	-
Dynamic GMM	4 models	-	-	-
Spatial models	5 models	-	-	4 models
Count data	9 models	-	Poisson	-
Discrete choice	9 models	-	-	-
Quantile regression	8 models	-	-	-
Stochastic frontier	2 models	-	-	-
Panel VAR/VECM	2 models	-	-	-
Diagnostic tests	50+	~5	~5	~10
Interactive charts	35+	-	-	-
Robust SE types	8	4	3	2
Bundled datasets	103	10	5	-

Requirements

• Python >= 3.9
• NumPy, Pandas, SciPy, statsmodels, scikit-learn
• Plotly, Matplotlib, Seaborn (visualization)
• Numba, Joblib (performance)

See pyproject.toml for full dependency list.

Documentation

• User Guide - Comprehensive guides for all model families
• API Reference - Full API documentation
• Tutorials - Interactive Jupyter notebooks
• Examples - 80+ example notebooks across all model families
• Theory - Econometric theory guides
• Benchmarks - Validation against R/Stata

Citation

@software{panelbox2026,
  author = {Haase, Gustavo and Dourado, Paulo},
  title = {PanelBox: Panel Data Econometrics in Python},
  year = {2026},
  version = {1.0.0},
  url = {https://github.com/PanelBox-Econometrics-Model/panelbox}
}

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE.

Support

• GitHub Issues
• Documentation
• Discussions
• Changelog

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Made with care for econometricians and researchers