Syllabus
Spring 2022 Econometrics - Glasgow program
Instructors:
Week 1 - 10
PhD in Statistics and Applied Probability, University of California-Santa Barbara (UCSB)
Assistant Professor, Institute of Quantitative Economics, ECON, NKU
Office: School of Economics, 1415
Email: jiayexu@nankai.edu.cn
Week 11 - 17
Dr. Li Hao (李好)1
Email: TBD
Lecture Time and Location:
Tue 14:00 - 16:15 (without a break),
No.2 Classroom Building (圆阶) 201
Office Hours:
Week 1 - 10
Dr. Jiaye Xu, Tue 16:30 - 17:30
Please feel free to come to my office hours to ask questions and provide feedback on the class.
Textbooks
Week 1 - 10
There is no required textbook. Lecture materials are self-contained.
Some reference books that may be helpful:
Applied Linear Regression by Weisberg, Sanford, 4th ed. Wiley (2013)
Linear Models with R (2nd ed) by Julian Faraway
Week 11 - 17
TBD
Course Topics
Week 1 - 10
Simple and multiple linear regression, analysis of variance (ANOVA), transformations, variable and model selection including stepwise regression, with emphasis on real-data data analyses with R packages. If time allows, some topics in generalized linear modeling will be covered.
Week 11 - 17
TBD
Prerequisites
Basic knowledge of probability, statistical theory, and applied statistics.
Computing
Week 1 - 10
We will use the open source statistical software R, available at http://www.r-project.org, and the open source & productive integrated development environment (IDE) RStudio that can be downloaded from https://www.rstudio.com/.
Some R examples will be shown in class with limited programming details.
Week 11 - 17
TBD
Assessment
The breakdown of grading in this course:
60% Module I + 40% Module II
- Module I (60%): covers all topics in Week 1-9.
- Project (30%): Project will be a report of real data analysis. No late project will be accepted after the due date. Due date and the way to submit the project will be announced later.
- Midterm (30%): Midterm will be an in-class exam in Week 10.
- Module II (40%): covers all topics in Week 11-17.
Academic Honesty and Integrity
Any work submitted to fulfill an academic requirement must represent your original work. You are welcome to discuss approaches to project problems with your classmates from different groups, but you must write report on your own and produce your own R code. No collaboration is allowed during the take-home exams. Only handwritten cheat sheet (formula sheet) is allowed during the in-class exams. Any student caught cheating will receive an ‘0’ for this course. 《南开大学研究生学则（2017版）》
Footnotes
Dr. Li is the official instructor of this course according to Office of Registrar.↩︎