regression-analysis

Panel data regression with fixed effects in R uses the fixest package and its feols function. This Skill guides you through confirming model specifications, validating variables, estimating fixed effects models, and running post-estimation diagnostics to ensure robust econometric results.

Post-estimation diagnostics for econometric models should include residual analysis, variance inflation factor checks, heteroskedasticity tests, and Cook's distance calculations. This workflow validates assumptions and identifies influential observations after estimating your regression with fixest.

Exporting regression results to LaTeX tables uses the etable function from the fixest package. This Skill produces export-ready output for reporting after completing data validation, model estimation, and post-estimation diagnostics on your panel dataset.

Yes, fixest supports instrumental variable estimation, difference-in-differences models, and related causal inference techniques for panel data. This Skill guides you through the workflow of specifying these models, selecting proper standard errors, and validating econometric assumptions.

The standard econometric regression workflow involves data validation, model specification confirmation, estimation with fixest, post-estimation diagnostics, and LaTeX-ready output via etable. This Skill structures the process using modular functions and proper standard error selection across diverse panel datasets.

Variable validation and sampling checks ensure clean inputs before estimating fixed effects regressions. This Skill provides step-by-step specification confirmation and data validation routines to verify your panel dataset is properly structured for econometric modeling with fixest.