ECON 2312Q - Econometrics II - Spring 2023

Instructor: Jiaqi Wang

Office: OAK 320

Office hours: 11:30 AM - 1:00 PM, Tu/Th

Lecture: 9:30AM - 10:45AM, Tu/Th

Classroom: MCHU 302

📜 Course Description

As an introductory course in econometrics, this course teaches basic econometric methods and related R programming skills. The course begins with a review of basic probability theory, the simple regression model, and ordinary least squares (OLS). The course then discusses the endogeneity problem in empirical economic research and several popular econometric tools that can be used to mitigate the problem, such as the panel data model, difference-in-difference, fixed effects model, instrumental variables, etc. The course concludes with the limited dependent variable models, including probit and logit models.

🗝 Course Objectives

By taking this course,

students should develop an intuition for the endogeneity problem. When facing an empirical problem, students should be able to figure out the potential sources of endogeneity and be able to pick useful econometric methods to mitigate the problem.
students should also be able to understand advanced econometric tools, especially their assumptions, and areas of application. Students should be able to read mathematical derivations presented in the course.
students should be able to apply basic R programming skills, such as data importing, data cleaning, and data visualization. Students should be able to use R to apply econometric methods from this course to empirical data as well.

📚 Readings and Textbooks

Readings

JMW and SW are very similar in terms of contents, students may focus on one of it if time is limited. The course will cover examples from both textbooks.

⌛Course Schedule

Tentative Topics

🏆 Grading

Breakdown

Assignments: 40%
Pop Quizzes: 10%
Midterm: 25%
Final Exam: 25%

Letter Grades

A ≥90

A- [80,90)

B+ [75,80) B [70,75)

B- [65,70)

C+ [60,65)

C [55,60)

C- [50,55) D [45,50)
F <45

Assignments

There will be 6-8 assignments depending on the course progress. For some assignments, students will type their answers in HuskyCT. Some questions may ask students to work on data with R. Students need to submit their coding scripts as well. The detailed instruction will be given when the assignment is posted.