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r-empirical-finance

Enforce empirical-finance R standards for panel data analysis with fixest.

148|30|Updated Mar 26, 2026
Search Tags:#causal inference#reproducible research#R#data.table#fixest#panel data#event study
One-click install
npx skills add https://github.com/aspi6246/Claude-Code-Skills-for-Academics --skill r-empirical-finance
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: r-empirical-finance
Source: https://github.com/aspi6246/Claude-Code-Skills-for-Academics/tree/main/r-empirical-finance
Command: npx skills add https://github.com/aspi6246/Claude-Code-Skills-for-Academics --skill r-empirical-finance

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

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.

Frequently Asked Questions about r-empirical-finance

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

FAQPage Schema
How do I run clustered standard errors for panel data in R?▼

Clustered standard errors for panel data in R are enforced by using fixest functions like feols and explicitly specifying the vcov clustering choice to ensure methodologically appropriate estimation.

What is the best way to load large datasets for reproducible empirical finance in R?▼

The best way to load large datasets for reproducible empirical finance in R is using arrow/Parquet or fread for data ingestion, standardizing panel-data keys and duplicate detection during the loading process.

How do I implement a difference-in-differences or event study using fixest?▼

Difference-in-differences and event studies using fixest require feols estimation with clustered standard errors, parallel-trends checks for DiD, and Sun & Abraham event study patterns to prevent common methodological pitfalls.

Can I use base lm() for panel data regressions with fixed effects?▼

Using base lm() for panel data regressions without fixed effect absorption is flagged as a high-risk issue, and fixest is strongly recommended instead to properly handle fixed effects and clustered standard errors.

How do I create publication-ready regression tables and plots in R?▼

Publication-ready regression tables and plots in R are created using fixest's etable for formatted outputs and ggplot2 for clean visualizations with consistent labeling, captions, and standardized reporting conventions.

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