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

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Panel Data Analysis in Python

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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