Event Study CARs is a Data skill for Claude Code, published by kennethkhoocy in applied-micro-skills.
Computes publication-quality cumulative abnormal returns (CARs) and CAAR with robust event-study test statistics (Patell, BMP, GRANK-T, etc.). Use for event studies, thin-trading adjustment, and market-model regressions.
Event Study CARs is cataloged under Data on DirSkills. Event Study CARs comes from a repository tagged applied-microeconomics, claude-code, claude-skills, codex and codex-skills.
Documentation
A complete methodology reference for computing publication-quality CARs with robust test statistics, matching the rigor of Kaspereit's eventstudy2 (v3.2b) for Stata. This skill is generic — applicable to any market, asset class, or event type.
This is the opening of the README. Read the full README on GitHub.
Event Study CARs is one of 25 skills that DirSkills catalogs from kennethkhoocy/applied-micro-skills, the repository it ships in. Its siblings there include Adversarial Empirical Review, Annotator Input Parity Check and AsyncOpenAI Concurrency Fix. Each one is a separate skill with its own page in this directory, installs the same way Event Study CARs does, and is maintained by kennethkhoocy in that same repository. The rest of the collection is listed on the kennethkhoocy/applied-micro-skills page.
Event Study CARs ranks #627 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Event Study CARs against them. Open each page to compare what they document and how they install.
Event Study CARs is one of 25 skills cataloged on DirSkills from kennethkhoocy/applied-micro-skills.
Event Study CARs is one of 668 Data skills cataloged on DirSkills, ranked #627 by stars among them.