panel-data

To estimate fixed effects and random effects models for panel data in Python, use the linearmodels library to fit within-entity and GLS specifications on a MultiIndex (entity, time) DataFrame. The workflow supports clustered standard errors and side-by-side regression comparison tables.

The best way to run a Hausman specification test is to estimate both fixed and random effects models, then generate decision statistics comparing their coefficients. This skill computes the Hausman test directly using linearmodels outputs to determine the appropriate model specification.

To fit Arellano-Bond dynamic panel GMM models with Python, use the linearmodels package to apply IV-style workflows on entity-time datasets. This skill handles dynamic persistence modeling, allowing you to estimate lagged dependent variable specifications with robust inference.

Yes, linearmodels requires a specific DataFrame format for panel econometrics: inputs must be provided as a MultiIndex (entity, time) DataFrame. This structure allows the library to correctly identify cross-sectional units and time periods for fixed effects and dynamic panel models.

To compute clustered and robust standard errors for pooled OLS panel regression, fit the model using linearmodels with appropriate covariance settings. This skill configures clustering at the entity level and aligns coefficients to generate reliable inference statistics for firm-year datasets.

After estimating fixed effects and dynamic panel models, you should run panel diagnostics such as unit-root testing and serial correlation checks, including Wooldridge-style tests. This skill performs these validation checks alongside Hausman tests to verify model specification and persistence assumptions.