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Instructor: รศ. ดร. วีระชาติ กิเลนทอง (tee@riped.utcc.ac.th) และ ดร.สัจจา ดวงชัยอยู่สุข (kei@riped.utcc.ac.th)
Course Schedule: Wednesday 18.00–21.00 pm Room 5303 and Saturday 13.00–16.00 Room 5401
TAs: คุณธนาธร มหาโยธา (ThanathonM.riped@gmail.com) และคุณณัฐนันทน์ ใจสะอาด (nutthanun.riped@gmail.com)
1. Course Objective
1. Course Objective
This course aims to provide basic and essential tools in econometrics for financial engineering students. It covers simple regression model; mutiple regression model (e.g., estimation, inference, asymptotic properties, specification issues, endogeneity problems, standard errors with heteroskedasticity); instrumental variable approach; simple panel data methods; and advanced panel data methods. This course combines lecture, theoretical and practical assignments, and project presentation to ensure that students will be able to grasp basic and essential tools in econometrics.
2. Required Textbooks:
1. Wooldridge, F.M. (2020). Introductory Econometrics: A Modern Approach (7th Edition). CENGAGE.
Data Sources
We will provide relevant data through the course website: https://sites.google.com/riped.org/tee/teaching/econometrics
Program Sources
3. Grades and Requirements
3. Grades and Requirements
Grades will be based on the following weights:
30% Assignments + Quizzes
40% Porject Presentation
30% Final Exam
Tentative Grading Range:
85 – 100 A
80 – 84 B+
70 – 79 B
65 – 69 C+
55 – 64 C
50 – 54 D+
40 – 49 D
39 or less F
3.1. Assignment
3.1. Assignment
Students will be assigned to complete approximately 10 individual assignments during the semester. An assignment with the lowest score will be dropped when calculating the total score for each student. Note: Late submission of the assignments is not accepted; a score of zero will be recorded for such assignment.
3.2. Examination
3.2. Examination
There is one examination: the final exam counting for 30% of the total points. If a student misses the examination without acceptable excuse, a score of zero will be recorded for the examination.
Course Schedule
The course will be carried out in 15 sessions, totalling 45 lecture hours. The structure of the course is subject to revision if necessary (e.g., to conform to the background, knowledge, and interests of the students). The tentative structure of the whole course is as follows:
Class 1 (WED June 22, 2022) : Introduction to Econometrics.
Class 2 (SAT June 25, 2022) : Simple Regression Model.
Class 3 (SAT July 2 (morning), 2022) : Mutiple Regression Model: Estimation I.
Class 4 (SAT July 2 (afternoon), 2022) : Mutiple Regression Model: Estimation II.
Class 5 (WED July 6, 2022) : Mutiple Regression Model: Inference I.
Class 6 (SAT July 9, 2022) : Mutiple Regression Model: Inference II.
Class 7 (WED July 13, 2022) : Mutiple Regression Model: Asymptotics.
Class 8 (SAT July 16, 2022) : Mutiple Regression Model: Further Issues.
Class 9 (WED July 20, 2022): Heteroskedasticity.
Class 10 (SAT July 23 (morning), 2022) : Specification and Data Issues.
Class 11 (SAT July 23 (afternoon), 2022) : Instrumental Variable Approach.
Class 12 (WED August 3, 2022) : Review.
Final Exam (August 6 2022)
Class 13 (SAT August 13 (morning), 2022) : Project Presentations
Class 14 (SAT August 13 (afternoon), 2022) : Project Presentations
Class 15 (SUN August 14 (morning), 2022) : Project Presentations
Problem Assignments
1. Problem Assignment 1 (Due on June 25, 2022 at the beginning of the class).
2. Problem Assignment 2 and Dataset for Assignment (Due on July 2, 2022 at the beginning of the class).
3. Problem Assignment 3 and Dataset for Assignment (Due on July 6, 2022 at the beginning of the class).
4. Problem Assignment 4 and Dataset for Assignment (Due on July 9, 2022 at the beginning of the class).
5. Problem Assignment 5 and Dataset for Assignment (Due on July 13, 2022 at the beginning of the class).
6. Problem Assignment 6 and Dataset for Assignment (Due on July 16, 2022 at the beginning of the class).
7. Problem Assignment 7 and Dataset for Assignment (Due on July 20, 2022 at the beginning of the class).
8. Problem Assignment 8 and Dataset for Assignment (Due on July 23, 2022 at the beginning of the class).
9. Problem Assignment 9 and Dataset for Assignment (Due on August 3, 2022 at the beginning of the class).
Computer Codes
The course will STATA program as the main statistical tool. Here are slides presentations for the STATA coding tutorials.
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