# Welcome ### About this Gitbook Welcome to my GitBook project “Applied Panel Analysis Using Stata”. This project stems from my master's program in Longitudinal Data Analysis at the Department of Sociology at the University of Munich (LMU). Although the course emphasized the value of panel data in social science research using Stata as a tool, as a student with a keen interest in the logic and derivation of models, I always felt that mastering software commands alone was not enough to truly understand the essence of panel measures. Therefore, the goal of this GitBook is to preserve the model formulas, derivation intuitions, and econometric frameworks that I believe are essential without being overly cumbersome, while supplementing them with hands-on commands and explanations of the results output in Stata. The structure of the chapters in this book largely corresponds to the outline of the course handouts, starting with basic panel data structures and estimation intuition, and progressing to fixed and mixed effects models, nonlinear models, event history analysis, and even the challenges and limitations of causal inference. The chapters are interspersed with Stata examples, mathematical formulas, and some of my own notes and reflections. Of course, the book is still being updated. All of the content is organized and rewritten by me based on the content of the course and other time series analysis textbooks, if there are any omissions or misunderstandings, please feel free to point them out. If you want, you can also fork this project to add notes, feedback, or content expansion. *** ### About the Author Shuangyang Wang LMU Munich May 2025 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://shuangyang-wang.gitbook.io/applied-panel-analysis-using-stata/readme.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.