Guide Stata users through data management, statistical analysis, and programming.
npx skills add https://github.com/dylantmoore/stata-skill --skill stata-dylantmoore
Please help me install this Agent Skill. Skill: stata Source: https://github.com/dylantmoore/stata-skill/tree/main/skills/stata Command: npx skills add https://github.com/dylantmoore/stata-skill --skill stata-dylantmoore
This Skill provides comprehensive guidance and examples for using Stata, enabling users to write, debug, and understand Stata code effectively.
csdid or did_multiplegt for robust estimation and plotting.Use the stata skill to explain how to perform a difference-in-differences analysis with staggered treatment adoption.
High-intent search queries and answers about installing and using this skill.
To run a difference-in-differences analysis with staggered treatment adoption in Stata, use modern community packages like `csdid` or `did_multiplegt` for robust estimation and plotting. These packages help avoid pitfalls associated with traditional two-way fixed effects (TWFE) regression by providing accurate treatment effect estimates under staggered rollout designs.
The best way to clean and reshape panel data for econometrics in Stata is to use built-in data management commands for importing, transforming, merging, and reshaping datasets. This skill guides you through detailed data manipulation techniques to ensure your panel data structure accurately supports fixed-effects and random-effects statistical models.
Yes, you can use Mata for advanced programming and class definitions in Stata. This skill explains Stata's programming capabilities, including how to utilize macros, write custom programs, and leverage Mata's matrix programming language to build classes and execute complex mathematical computations efficiently.
Stata handles causal inference and regression analysis by providing built-in statistical commands alongside detailed guidance on econometric methods. It supports core syntax for descriptive statistics, panel data analysis, time series modeling, and advanced causal inference techniques, helping you estimate treatment effects and interpret results accurately.
Yes, there are community packages available for creating regression tables and plotting in Stata. This skill offers detailed explanations of specialized community-contributed commands designed to format regression outputs into publication-ready tables and generate robust visual graphics for data presentation.
Your two-way fixed effects (TWFE) regression fails with staggered treatment timing because TWFE can produce negative weighted averages of treatment effects under staggered adoption. To resolve this, use modern difference-in-differences packages like `csdid` or `did_multiplegt` which correctly handle staggered treatment designs.
meleantonioClean and transform messy Stata datasets into analysis-ready data with do-files.
meleantonioConvert regression outputs from Stata, R, or Python into LaTeX tables.
fredmilhomeConduct difference-in-differences event study analysis with doubly robust estimators.
zhangzhang-111-iDesign A/B tests, engineer features, and evaluate models with MLflow.