Barrios Skills — John Barrios's curated workflow for economists and accountants. Prioritize reproducible empirical work, clear identification language, and journal-ready output.

Python Panel Data

Purpose

This skill helps economists run panel data models in Python using pandas, statsmodels, and linearmodels, with correct fixed effects, clustering, and diagnostics.

When to Use

• Estimating fixed effects or random effects models
• Running difference-in-differences on panel data
• Creating regression tables and plots in Python

Instructions

Follow these steps to complete the task:

Step 1: Understand the Context

Before generating any code, ask the user:

• What is the unit of observation and panel identifiers?
• Which outcomes and regressors are required?
• What fixed effects or time effects are needed?
• How should standard errors be clustered?

Step 2: Generate the Output

Based on the context, generate Python code that:

1. Loads and cleans the data with pandas
2. Sets a MultiIndex for panel structure
3. Fits the model using linearmodels.PanelOLS or RandomEffects
4. Outputs results in a readable table and optional LaTeX

Step 3: Verify and Explain

After generating output:

• Interpret key coefficients
• Note assumptions (strict exogeneity, parallel trends, etc.)
• Suggest robustness checks (alternative clustering, placebo tests)

Example Prompts

• "Run a two-way fixed effects model with firm and year effects"
• "Estimate a DiD using state and year fixed effects"
• "Export panel regression results to LaTeX"

Example Output

# ============================================
# Panel Data Analysis in Python
# ============================================
import pandas as pd
from linearmodels.panel import PanelOLS

# Load data
df = pd.read_csv("panel_data.csv")

# Set panel index
df = df.set_index(["firm_id", "year"])

# Create treatment indicator
df["treat_post"] = df["treated"] * df["post"]

# Two-way fixed effects model
model = PanelOLS.from_formula(
    "outcome ~ 1 + treat_post + EntityEffects + TimeEffects",
    data=df
)
results = model.fit(cov_type="clustered", cluster_entity=True)

print(results.summary)

Requirements

Software

• Python 3.10+

Packages

• pandas
• linearmodels
• statsmodels

Install with:

pip install pandas linearmodels statsmodels

Best Practices

1. Always verify panel identifiers and balanced vs unbalanced panels
2. Cluster standard errors at the appropriate level
3. Check for missing data before estimation

Common Pitfalls

• Failing to set a proper panel index
• Using pooled OLS when fixed effects are required
• Misinterpreting coefficients without accounting for fixed effects

References

• linearmodels documentation
• statsmodels documentation
• Wooldridge (2010) Econometric Analysis of Cross Section and Panel Data

Changelog

v1.0.0

• Initial release