Instructor: Anthony Tay
SOE 5017.4 (5th floor) 6828-0850
email: anthonytay@smu.edu.sg
Consultation Hours: Tuesday 1530-1730 / email for appointment
Teaching Assistant: Zhu Xiaofan
email: xiaofan.zhu.2020@phdecons.smu.edu.sg
Consultation Hours: email for appointment
AY2024/25 Class Time and Venue: Tuesday 1200 - 1515 SOE/SCIS2 Seminar Room 3.7
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: ▸ Exam 40% ▸ Weekly Review Questions 20% ▸ Assignments 30% ▸ Class and Forum Participation 10%
Recommended Textbooks:
There are no required textbooks for this course. The main material are the slides. If you wish to read further the topics discussed, I recommend the following:
▸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. Econometrics Notes (Old draft, pdf version) These notes are undergoing a major update, which is not yet complete. We do not need to use these notes for this course, except for Chapter 1. Should you decide to use these notes, please be aware that there are substantial differences in notational conventions in the notes and in the slides.
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
Topics:
1. Statistics Review (Corrections made to slides; New version: 20 Aug 2024)
▸ Slides (1 per page)
▸ Slides (2 per page)
▸ Slides (4 per page)
▸ Session 1 Review Exercise
(Due 2300 Sunday 25 Aug)
▸ Data: earnings2019.csv
▸ Reference: MPQE Chapter 7
The given reference is Chapter 7 of the R Version of my in-progress textbook "Mathematics and Programming for the Quantitative Economist", with Ismail Baydur and Daniel Preve. Solutions to exercises at the end of the chapter.
Regarding the weekly Review Exercise sets. (Updated 21 Aug 2024) There will be a set of Review Exercises for each session. This is 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 2.5 points (regardless of number of questions in the set). You will be given the full 2.5 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. Only the top 8 scores (out of 12) are counted. Absolutely no deadline extensions will be given.
2. Linear Regression Overview, Simple Linear Regression
▸ Slides (1 per page)
▸ Slides (2 per page)
▸ Slides (4 per page)
▸ Session 2 Review Exercise
(Due 2300 Sunday 1 Sep)
▸ Supplement: Session 2 Supplement (pdf)
▸ Session 2 Supplement (qmd, zipped)
▸ Data: earnings2019.csv (Same as Session 1)
3. Simple Linear Regression Standard Errors, Multiple Linear Regression
▸ Slides (1 per page)
▸ Slides (2 per page)
▸ Slides (4 per page)
▸ Session 3 Review Exercise (Replacement)
(Due 2300 Sunday 8 Sep)
▸ Session 3 Review Exercise B
(Due 2300 Sunday 15 Sep)
▸ Data: earnings2019.csv (Same as Session 1),
heterosk.csv,
multireg_eg.csv
4. Matrix Algebra (week 5) (Corrections made to slides; New version: 17 Sep 2024)
▸ Slides (1 per page)
▸ Slides (2 per page)
▸ Slides (4 per page)
▸ Session 4 Review Exercise
(Due 2300 Sunday 22 Sep)
▸ Data: causes-of-death-by-state.csv
▸ Reference: MPQE Chapter 4,
MPQE Chapter 8,
MPQE Chapter 10
5. OLS Using Matrix Algebra (week 6)
▸ Slides (1 per page)
▸ Slides (2 per page)
▸ Slides (4 per page)
▸ Session 5 Review Exercise
(Due 2300 Sunday 29 Sep)
▸ Data: earnings2019.csv (Same as Session 1)
6. Hypothesis Testing (Week 7)
▸ Slides (1 per page)
▸ Slides (2 per page)
▸ Slides (4 per page)
▸ Session 6 Review Exercise
(Due 2300 Sunday 6 Oct)
▸ Data: earnings2019.csv (Same as Session 1)
(Break Week -- Week 8)
7. Prediction with Regression Models (Week 9)
▸ Slides (1 per page)
▸ Slides (2 per page)
▸ Slides (4 per page)
▸ Session 7 Review Exercise
(Due 2300 Sunday 20 Oct)
▸ Data: earnings2019.csv (Same as Session 1), S7_Chisq1_samples.csv
8. Instrumental Variables and GMM Estimation (Week 10) (Corrections made to slides; corrections in blue; new version 22 Oct)
▸ Slides (1 per page)
▸ Slides (2 per page)
▸ Slides (4 per page)
▸ Session 8 Review Exercise
(Due 2300 Sunday 27 Oct)
▸ Data: earnings2019.csv (Same as Session 1)
9. Generalized Least Squares / Panel Data Models (Week 11) (Corrections made to slides; new version 29 Oct)
▸ Slides (1 per page)
▸ Slides (2 per page)
▸ Slides (4 per page)
▸ Session 9 Review Exercise
(Due 2300 Sunday 3 Nov)
10. Maximum Likelihood Estimation / Limited Dependent Variable Models (Week 12) (Corrections made to slides; new version 5 Nov)
▸ Slides (1 per page)
▸ Slides (2 per page)
▸ Slides (4 per page)
▸ Session 10 Review Exercise
(Due 2300 Sunday 10 Nov)
▸ Data: trunc_censored.csv
11-12. Introduction to Time Series Data / Times Series Regressions (Week 13)
▸ Slides (1 per page)
▸ Slides (2 per page)
▸ Slides (4 per page)
▸ Session 1 Review Exercise
(Due 2300 Sunday 17 Nov)
▸ Data: ts_01.xlsx,
ts_02.xlsx
Assignments:
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 3 (4 points) (Due: 2300 Sunday 17 Nov 2024)
▸ Assignment 2 (12 points) (Due: 2300 Sunday 27 Oct 2024)
▸ Assignment 1 (14 points) (Due: 2300 Sunday 29 Sep 2024)
▸ Data: earnings2019.csv (see Session 1), Anscombe.xlsx (see "Software" above)