Instructor:                  รศ. ดร. วีระชาติ กิเลนทอง (tee@riped.utcc.ac.th) และ  ดร.สัจจา ดวงชัยอยู่สุข (kei@riped.utcc.ac.th) 

Course Schedule:        Saturday 8.30–13.00 Room 5601 

TAs:                               คุณธนาธร มหาโยธา (ThanathonM.riped@gmail.com) 

1. Course Objective

This course aims to provide business economics students with basic and essential tools in econometrics. It covers simple regression models, multiple regression models (e.g., estimation, inference, asymptotic properties, specification issues, endogeneity problems, standard errors with heteroskedasticity), generalized least square (GLS), the instrumental variable approach,  maximum likelihood estimation (MLE), and advanced topics (e.g., panel data methods, errors in variables issues, limited dependent variable models). This course combines lectures and (both theoretical and empirical) assignments 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.

3. Grades and Requirements

Grades will be based on the following weights: 

40%         Assignments + Quizzes

20%         Midterm Exam 

40%         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

Students will be assigned to complete approximately 10 individual assignments during the semester. An assignment with the lowest score will be dropped when the total score for each student is calculated. Note: Late submission of the assignments is not accepted; a score of zero will be recorded for such assignments.

             3.2. Examination

There are two examinations, the midterm exam and the final exam, which count for 20% and 40% of the total points. If a student misses the examination without an acceptable excuse, a score of zero will be recorded for the examination. 

4. Course Schedule

The course will be carried out in 10 sessions, totaling 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:

1. Class 1 (SAT June 15, 2024) :  Introduction to Econometrics 

2. Class 2 (SAT June 22, 2024) :  Multiple Regression Model: Estimation-1

3. Class 3 (SAT June 29, 2024) :  Multiple Regression Model: Estimation-2

4. Class 4 (SAT July 6, 2024) :   Multiple Regression Model: Inference 

Mid-Term Exam (July 13, 2024)

5. Class 5 (SAT July 20, 2024) :  Asymptotic Analysis of Regression

6. Class 6 (SAT July 27, 2024) :  Issues with Regression Analysis

7. Class 7 (SAT August 3, 2024) :  Generalized Least Square (GLS) and Related Topics

8. Class 8 (SAT August 10, 2024) :  Instrumental Variable Estimation (IV)

9. Class 9 (SAT August 17, 2024) :  Maximum Likelihood Estimation (MLE)

10. Class 10 (SAT August 24, 2024) :  Advanced Topics

Final Exam (August 31, 2024)

5. Problem Assignments 

1. Problem Assignment 1 and Dataset for Assignment 1 (Due on June 22, 2024, at the beginning of the class)

2. Problem Assignment 2 and Dataset for Assignment 2 (Due on June 29, 2024, at the beginning of the class)

3. Problem Assignment 3 and Dataset for Assignment 3 (Due on July 6, 2024, at the beginning of the class)

4. Problem Assignment 4 and Dataset for Assignment 4 (Due on July 20, 2024, at the beginning of the class)

5. Problem Assignment 5 and Dataset for Assignment 5 (Due on July 27, 2024, at the beginning of the class)

6. Problem Assignment 6 and Dataset for Assignment 6 (Due on August 3, 2024, at the beginning of the class)

7. Problem Assignment 7 and Dataset for Assignment 7 (Due on August 10, 2024, at the beginning of the class)

8. Problem Assignment 8 and Dataset for Assignment 8 (Due on August 17, 2024, at the beginning of the class)

9. Problem Assignment 9 and Dataset for Assignment 9 (Due on August 24, 2024, at the beginning of the class)

10. Problem Assignment 10 and Dataset for Assignment 10 (Due on August 31, 2024, at the beginning of the class)

6. Data Sources and STATA Program

      We will provide relevant data through the course website: https://sites.google.com/riped.org/tee/teaching/basic-econometrics-for-econ

The course uses the STATA program (Program STATA version 14) as the main statistical tool. Here are the slides for the STATA coding tutorials.

1. Installing STATA and its basic functions

2. Simulation of multiple regression