The reading group will last for the whole semester, and will cover the following topics, mostly related to causal inference, or endogeneity problems in an OLS setting:
- Robust and clustered standard errors to achieve robust inference in an OLS model
- Instrumental variable approach to attack the endogeneity problems wide spread in OLS models
- Panel data with fixed effects to solve for the unobservable heterogeneity problem, an important source of endogeneity issues
- GMM approach for dynamic panels, where fixed effects lead to inconsistent estimates using OLS
- Difference-in-difference approach for policy evaluation or shock identification, one of the most popular method to overcome the endogeneity issues in recent years
- Regression discontinuity design, an equally popular method for endogeneity problems in recent years
- Quantile regression, especially useful to analyze inequality issues and uncover heterogeneous effects of explanatory variables
Registration
Contact me directly via Email, and the registration deadline is 9/16. The reading group is mainly for junior and fourth year undergraduates, seeking to write a summer camp paper or dissertation.
Prerequisite:
- Math: caculus, linear algebra, probability
- Stats: statistics, elementary econometrics (familiar with multi variable OLS estimation)
- Software: R, Stata
Each participant is required to contribute at least one presentation throughout the reading group
Reference books
- 邱嘉平，《因果推断实用计量方法》，2020 (REQUIRED)
- Angrist and Pischke, Mostly Harmless Econometrics, 2009
- 赵西亮，《基本有用的计量经济学》，2017
Organizaiton and introduction
We meet every Thursday afternoon 2:00 – 4:30 pm in Big Data Institute (BDI). The first group meeting will take place on September 17th.
We will have 2 – 3 presentations each week. All participants need to present at least once to contribute to the reading group.
The following is a rough schedule:
- Presentations of own research by participants from the previous year, 2 – 3 weeks.
- Presentations by new participants (mostly first year graduates and third year undergraduates) on causal inference methodology, 4 – 5 weeks.
- Presentations by contribution, on research plans including the empirical questions, data, methodology, and most importantly, key references from the literature, 4 – 5 weeks.
Presentations
- 9/17, introduction, and presentations of summer research by
- 程子帅
- 梁思靖
- 胡智慧
- 9/24, presentations of summer research and undergraduate thesis by
- 10/8, presentations of summer research; begin at 2:40pm instead of 2:00pm
- 10/15, presentations of summer research; begin at 3:00pm instead of 2:00pm
- 10/22, presentations of CI methodology
- 10/29, presentations of CI methodology
- 11/5, presentations of CI methodology
- 11/19, presentations of CI methodology; start at 1:00 pm instead of 2:00 pm
- 11/26, presentations of CI methodology
- 12/03, presentations of CI methodology
- 12/10, presentations of CI methodology
- 12/17, presentations of CI methodology
| 章节 | 主题 | 内容 | 主讲人 | 日期 |
|---|---|---|---|---|
| 第1章 | 因果推断概念 | 王健 | 10/22 | |
| 第2章 | 线性回归基础 | 第1-2节 | 相耐汀 | 10/22 |
| 第2章 | 线性回归基础 | 第3-4节 | 蔡诺璇 | 10/22 |
| 第3章 | 线性回归运用 | 第1-3节 | 李非凡 | 10/29 |
| 第3章 | 线性回归运用 | 第4-7节 | 李思芃 | 10/29 |
| 第4章 | 标准误差 | 第1-3节 | 李浩芸 | 10/29 |
| 第4章 | 标准误差 | 第4-6节 | 曾卉琪 | 11/5 |
| 第5章 | 处置效应 | 第1-4节 | 江梦瑜 | 11/5 |
| 第5章 | 处置效应 | 第5-8节 | 陈远致 | 11/5 |
| 第6章 | 匹配方法 | 第1-4节 | 朱思颖 | 11/19 |
| 第6章 | 匹配方法 | 第5-7节 | 侯天宇 | 11/19 |
| 第7章 | 匹配方法与回归方法对比 | 叶立欢 | 11/19 | |
| 第8章 | 面板数据 | 第1-4节 | 陈露滢 | 11/26 |
| 第8章 | 面板数据 | 第5-7节 | 成思扬 | 11/26 |
| 补充 | 动态面板GMM | 郭倩美 | 11/26 | |
| 第9章 | 双重差分 | 第1-3节 | 蒋涵琦 | 12/3 |
| 第9章 | 双重差分 | 第4-5节 | 王子萱 | 12/3 |
| 第10章 | 工具变量 | 第1-3节 | 马兆星 | 12/3 |
| 第10章 | 工具变量 | 第4-7节 | 李竞开 | 12/10 |
| 第11章 | 样本选择模型 | 第1-3节 | 杨宇彤 | 12/10 |
| 第11章 | 样本选择模型 | 第4-6节 | 瞿博洋 | 12/17 |
| 第12章 | 断点回归 | 龙欣雨 | 12/17 |