Instructor: Anthony Tay
SOE 5025 (5th floor) 6828-0850
email: anthonytay@smu.edu.sg
Consultation Hours: Tuesday 1300-1500 / email for appointment
Teaching Assistant 1: Wei Jiezheng
email: jzwei.2022@phdecons.smu.edu.sg
Consultation Hours: email for appointment
Teaching Assistant 2: Gong Fanggfang
email: ffgong.2021@phdecons.smu.edu.sg
Consultation Hours: email for appointment
AY2024/25 Class Time and Venue: Tuesday 1530 - 1845 SOE/SCIS2 Seminar Room 3.3
Course Description: This course covers econometric models and techniques for predictive applications and causal inference in economics. The models covered range from linear regression models for cross-sectional and time series data, to panel data and limited dependent variable models. Estimation techniques include least squares methods, generalized method of moments, and maximum likelihood. Equal emphasis is placed on theory and application.
Learning Objectives: To gain an intermediate-to-advanced UG level understanding of the theory of least squares estimation and inference in the linear regression model ('the basic model'). To be able to assess the validity of the basic model and its assumptions given the properties and structure of the available data, and the purpose of the analysis. Where the basic model or its assumptions are inappropriate, to be able to apply the appropriate extensions and alternatives. To gain an introductory understanding of limited dependent variable models and panel data models.
Assessment: ▸ Weekly Review Exercises 30% ▸ Assignments 30% ▸ Exam 30% ▸ Class Participation 10%
Required Reading:
▸Tay, A. Econometrics Notes (1 Sept 2025 Version; Corrected typos. Next update expected end-September)
▸Tay, A. Econometrics Notes (html version)
Supplementary Reading:
▸Wooldridge, J.M., "Introductory Econometrics: A Modern Approach", 7th ed., Thomson/South-Western
▸Stock, J. H., and M.W. Watson, "Introduction to Econometrics", Pearson.
▸Tay, A., D. Preve and I. Baydur, "Mathematics and Programming for the Quantitative Economist (MPQE)"
Chapter 7,
Chapter 8
The MPQE chapters include sections on Python programming. Please ignore these. We will use R for this course. Students who are interested in reading the entire book, either for the math or the Python sections or both, please get in touch with me.
Software: R
Please read Chapter 1 of my "Econometrics Notes" for instructions on installing R and RStudio, and for a quick introduction to using R. R programming will not be on the final examination, but it is required to complete assignments. ▸ Data: Anscombe.xlsx
Sessions:
1. Probability and Statistics Review
▸ Slides (Corrected version: 20 August 2025)
▸ Readings: Chapter 2 of Econometrics Notes; Chapter 7 of MPQE (optional)
▸ Data: earnings2019.csv
For Sessions 2 to 11 there will be Weekly Review Exercise sets to encourage you to keep up with the class. These are to be completed in your own handwriting. Upload a scanned copy or photograph of your solutions to the eLearn coursepage by the due date. Each review exercise set carries 3 points (regardless of number of questions in the set). You will be given the full 3 points if you are deemed to have made a good effort in attempting the exercise set, regardless of the correctness of your submission. No detailed feedback will be given for these exercises but full solutions will be provided after the due date in the "content" section of the course eLearn page. No deadline extensions
2. Conditional Expectation, Simple Linear Regression
▸ Slides
▸ Session 2 Review Exercise (corrected)
▸ Readings: Chapter 3 of Econometrics Notes; Chapter 7 of MPQE (optional)
▸ Data: earnings2019.csv (Same as Session 1), heterosk.csv,
3. Multiple Linear Regression
▸ Slides (Corrected version: 2 September 2025)
▸ Session 3 Review Exercise
▸ Readings: Chapter 4 of Econometrics Notes;
▸ Data: earnings2019.csv (Same as Session 1), multireg_eg.csv
4. Matrix Algebra
▸ Slides (Corrected version: 17 September 2025)
▸ Session 4 Review Exercise
▸ Readings: Chapter 5 of Econometrics Notes; Chapter 8 of MPQE (optional)
5. OLS Using Matrix Algebra
▸ Slides (Corrected version: 17 September 2025)
▸ Session 5 Review Exercise
▸ Readings: Chapter 6 of Econometrics Notes
▸ Data: earnings2019.csv (Same as Session 1)
6. Least Squares Topics
▸ Slides
▸ Session 6 Review Exercise
▸ Readings: Chapter 7 of Econometrics Notes
▸ Data: earnings2019.csv (Same as Session 1)
7. IV Estimation and GMM
▸ Slides (Corrected version: 13 October 2025)
▸ Session 7 Review Exercise
▸ Readings: Chapter 8 of Econometrics Notes
▸ Data: earnings2019.csv (Same as Session 1)
8. Introduction to Time Series Econometrics 1
▸ Slides
▸ Session 8 Review Exercise
▸ Readings: Slides only
▸ Data: ts_01.xlsx, ts_02.csv
9. Introduction to Time Series Econometrics 2
▸ Slides
▸ Session 9 Review Exercise
▸ Readings: Slides only
▸ Data: ts_01.xlsx, ts_02.xlsx
10. Generalized Least Squares / Panel Data Models
▸ Slides
▸ Session 10 Review Exercise
▸ Readings: Slides only
▸ Data: promo2.csv
11. Maximum Likelihood Estimation / Limited Dependent Variable Models
▸ Slides
▸ Session 11 Review Exercise
▸ Readings: Slides only
▸ Data: trunc_censored.csv
12. Review Session
Assignments:
Three Assignments in total. Assignments to be done as a Quarto / R Markdown document and compiled to PDF. Here is the ▸ Quarto Template for Assignments. After "Rendering", the template compiles into this ▸ PDF Document
▸ Assignment 1 (Release: 1 Sep, Due: 21 Sep)
▸ Assignment 2 (Release: 29 Sep, Due: 19 Oct)
▸ Assignment 3 (Release: 28 Oct, Due: 16 Nov)