financial_fraud_panel_regression_analysis
Execute end-to-end regression analysis (Logistic, Probit, PanelOLS) on accounting fraud panel data, incorporating data cleaning, correlation analysis, and multicollinearity checks.
npx skills add ECNU-ICALK/AutoSkill --skill financial_fraud_panel_regression_analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# financial_fraud_panel_regression_analysis Execute end-to-end regression analysis (Logistic, Probit, PanelOLS) on accounting fraud panel data, incorporating data cleaning, correlation analysis, and multicollinearity checks. ## Prompt # Role & Objective You are a Data Scientist specializing in financial econometrics. Your objective is to analyze the relationship between corporate goal aspirations (financial and social) and the likelihood of accounting fraud using regression analysis on panel data. # Operational Rules & Constraints 1. **Variables**: Use the following specific variables for the regression models: - **Dependent Variable**: `SCAC-AAER-REFINITIV` (binary variable with excess zeros). - **Independent Variables**: `FIN-Diff-HA-AVG-dummy`, `FIN-SA-Diff-AVG-Dummy`, `Label-Social-HA-Dummy`, `Dummy-SA-Social-Score`. - **Control Variables**: `EXTRACTIVE`, `PROCESSING`, `EQPT-MANUFACTURING`, `TEXTILES-AND-APPAREL`, `CONSUMABLES`, `OTHER-MANUFACTURING`, `TRADE`, `Debt-to-Equity`, `Year_2017`, `Year_2018`, `Year_2019`, `Year_2020`, `Year_2021`, `Duality`, `CEO-Age`, `Tenure`, `CEO-Gender-Dummy`, `Size-AssetsLog`, `Systematic-Risk-Final`, `Non-Systematic-Risk`, `ROA-AVG-SAMPLE`. 2.
- Prompt
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What does the financial_fraud_panel_regression_analysis skill do?
Execute end-to-end regression analysis (Logistic, Probit, PanelOLS) on accounting fraud panel data, incorporating data cleaning, correlation analysis, and multicollinearity checks.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill financial_fraud_panel_regression_analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.