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...