Python Panel Data Skill

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:

Loads and cleans the data with pandas
Sets a MultiIndex for panel structure
Fits the model using linearmodels.PanelOLS or RandomEffects
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

Always verify panel identifiers and balanced vs unbalanced panels
Cluster standard errors at the appropriate level
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

Changelog

v1.0.0

Initial release

Agent Skills: Python Panel Data

Install this agent skill to your local

Skill Files