Econometric Learning Resources
1. Difference-in-Difference 前沿进展
This repository tracks the recent developments and innovations in the Difference-in-Difference (DiD) literature. It serves two purposes. First, it is an organized collection of various bookmarks from Twitter, GitHub, YouTube etc. Second, it aims to present the different packages from an end-user’s perspective. This part has to do with how to apply these methods in day-to-day applied research. On the theory side, several really useful resources are listed in the Resources including workshops and notes by some of the key authors leading the development in this field. Please refer to this section if you want a deeper theoreical understanding.
Recently, it seems like barely a week goes by before there’s a new paper on difference-in-differences, two-way fixed-effects estimators, and event study research methods. If you’re anything like me then you’ve got a folder with a bunch of these papers in it but haven’t gotten around to digging into them yet. Maybe you’ve been pointed to them (or have done the pointing) in referee reports.
I figured that there was probably a bunch of people in the same boat (try over 550 in the Slack alone) and so in spring 2021, I organized reading group to dig into this new literature. Many authors of these papers graciously agreed to present, with an eye towards the more applied audience of the reading group. The meetings were recorded and are available on Youtube. After the sessions I would try to work through some simple examples of implementation in R or Stata, the example code is available on Github.
2. 因果推断资料
因果推理课程、书籍、库、会议、博客、研究论文和行业应用的精选列表。
3. 因果推断书籍
This is the online version of Causal Inference: The Mixtape
Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.
The Effect is a book intended to introduce students (and non-students) to the concepts of research design and causality in the context of observational data. The book is written in an intuitive and approachable way and doesn’t overload on technical detail. Why teach regression and research design at the same time when they are fundamentally different things? First learn why you want to structure a design in a certain way, and what it is you want to do to the data, and then afterwards learn the technical details of how to run the appropriate model.
This book consists of a Part 1 dedicated to research design and causality, making use of causal diagrams to make the concept of identification straightforward, and a Part 2 dedicated to implementation and common research designs like regression with controls and regression discontinuity. You can see the chapters and navigate between them on the left (on in the dropdown menu up top if you’re on a small screen).
4. 视频课程
Applied Methods PhD Course
耶鲁大学博士应用经济学课程，课程资料地址在Github仓库