r-empirical-finance

What problem does it solve?

This Skill prevents common empirical-finance R mistakes by enforcing consistent panel-data conventions, estimation defaults, and output/plotting practices so your analysis is reproducible and methodologically defensible.

Core Features & Use Cases

• Panel-data correctness checks: standardizes keys, duplicate detection, and panel-structure verification after merges and data loading.
• Methodologically appropriate estimation: guides users toward fixest (feols/feglm/fepois), strongly nudges clustered standard errors for panel settings, and provides patterns for Sun & Abraham event studies, DiD, and IV/2SLS.
• Reliable data pipelines and reporting: encourages arrow/Parquet and fread-based ingestion for large data, plus publication-ready tables (etable) and clean ggplot2 output with consistent labeling and captions.
• Common gotcha prevention: flags high-risk issues like using lm() without FE absorption, missing vcov clustering, and skipping parallel-trends checks for DiD or first-stage diagnostics for IV.

Quick Start

Ask the AI to rewrite your R script to follow the r-empirical-finance standards for panel-data loading, duplicate checks, fixest clustered regressions, and publication-ready tables/figures.