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.

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.

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.

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.

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.

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.