panel-data

Guide econometrics researchers in selecting and diagnosing panel data models.

6|1|Updated Apr 3, 2026

Search Tags:#econometrics #panel #fixed-effects #random-effects #panel-data #gmm #hausman

Ready to run

npx skills add https://github.com/zhouziyue233/great-econometrics --skill panel-data-zhouziyue233

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: panel-data
Source: https://github.com/zhouziyue233/great-econometrics/tree/main/skills/panel-data
Command: npx skills add https://github.com/zhouziyue233/great-econometrics --skill panel-data-zhouziyue233

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Analyzes panel data correctly by addressing unobserved heterogeneity and guiding model selection (FE vs RE) with appropriate diagnostics.

Core Features & Use Cases

FE and RE model selection guidance with Hausman tests
Mundlak/Correlated Random Effects (CRE) framework
Two-way fixed effects, Arellano-Bond GMM, and robust standard errors
Panel survival models and LDV considerations across macro and micro panels

Quick Start

Load your panel data, decide whether to use fixed or random effects, and run a Hausman test to select the appropriate specification.

Frequently Asked Questions about panel-data

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

FAQPage Schema

How do I choose between fixed and random effects for panel data?▼

Choose between fixed and random effects by running a Hausman test on your panel data. If the test rejects the null hypothesis, fixed effects estimates are consistent and preferred to address unobserved heterogeneity; otherwise, random effects applies.

What is the Mundlak Correlated Random Effects approach for panel models?▼

The Mundlak or Correlated Random Effects (CRE) approach is a panel data model specification that includes the time-averages of independent variables. It allows random effects to yield identical estimates to fixed effects while recovering time-invariant coefficient estimates.

Can I estimate dynamic panel data with Arellano-Bond GMM in Python?▼

Yes, you can estimate dynamic panel data with Arellano-Bond GMM in Python using the linearmodels package. This Skill guides Arellano-Bond GMM specification alongside robust standard error adjustments for macro and micro panels.

Do I need specific packages to run panel data diagnostics in R or Stata?▼

Yes, you need specific econometrics packages like plm in R to run panel data diagnostics and model selection. These environments provide the necessary functions to execute Hausman tests, Wooldridge diagnostics, and robust standard error calculations.

How do I handle unobserved heterogeneity when analyzing macro and micro panels?▼

Handle unobserved heterogeneity in macro and micro panels by applying fixed effects, two-way fixed effects, or the Mundlak CRE framework. Apply robust standard errors like Driscoll-Kraay to ensure valid inference across different panel structures.

When should I use Driscoll-Kraay standard errors in panel data regression?▼

Use Driscoll-Kraay standard errors in panel data regression when errors exhibit cross-sectional dependence and heteroskedasticity. This approach provides robust inference for macro and micro panels where cross-sectional correlation violates standard OLS assumptions.