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

• linearmodels documentation

• statsmodels documentation

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

Changelog

v1.0.0

• Initial release