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python-panel-data

Estimate panel data regression models with fixed effects and clustered inference.

203|27|Updated Apr 19, 2026
Search Tags:#python#pandas#econometrics#panel data#linearmodels#clustered standard errors#fixed effects
One-click install
npx skills add https://github.com/franklee16/academic-research-skills --skill python-panel-data
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-panel-data
Source: https://github.com/franklee16/academic-research-skills/tree/main/data-analysis/python-panel-data
Command: npx skills add https://github.com/franklee16/academic-research-skills --skill python-panel-data

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It solves the problem of estimating econometric models on panel datasets without getting panel indexing, fixed effects, and clustered standard errors wrong.

Core Features & Use Cases

  • Panel Structure Setup (pandas MultiIndex): Organizes data into a proper panel format so models run on the intended entity and time dimensions.
  • Fixed/Random Effects Estimation (linearmodels): Fits PanelOLS and RandomEffects models to produce credible coefficient estimates with appropriate effects.
  • Inference & Diagnostics: Supports clustered covariance settings and helps interpret results with assumptions and robustness ideas.

Example Use

Estimate a two-way fixed effects model (e.g., firm and year effects) for a treatment implemented at the entity level over time, using clustered standard errors.

Quick Start

Ask the skill to estimate a two-way fixed effects (entity and time) model for your panel dataset and return a readable regression summary with clustered standard errors.

Frequently Asked Questions about python-panel-data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I estimate fixed effects panel data models with clustered standard errors in Python?▼

You estimate fixed effects panel data models with clustered standard errors by setting a pandas MultiIndex and applying linearmodels PanelOLS with cov_type clustering for correct entity and time inference.

What is the difference between fixed effects and random effects estimation for panel data?▼

Fixed effects estimation uses PanelOLS to control for time-invariant entity characteristics, while random effects estimation uses linearmodels RandomEffects to model entity-specific errors as random variables across panel datasets.

Can I run difference-in-differences analysis on an unbalanced panel dataset using linearmodels?▼

Yes, you can run difference-in-differences analysis on unbalanced panel datasets using linearmodels PanelOLS by properly structuring entity and time dimensions with pandas MultiIndex and applying two-way fixed effects.

How do I set up pandas MultiIndex for panel data regression in Python?▼

To set up pandas MultiIndex for panel data regression, organize your dataframe with entity and time dimensions as hierarchical indices so linearmodels can correctly identify fixed effects and apply clustered covariance.

When should I use clustered standard errors instead of standard robust errors for panel regression?▼

Use clustered standard errors in panel regression when error terms correlate within entities or over time, as clustering groups observations by entity to produce valid inference for fixed effects and difference-in-differences analyses.

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