Chulalongkorn University
: Master of Arts Program in Business and Managerial EconomicsEconomics 2949605 Chairat Aemkulwat
Quantitative Methods in Economic Analysis 1st Term, 2015
GENERAL INFORMATION: Classes is scheduled in a one-month module; tentative dates for lectures, tutorial sessions and examination are in the syllabus. Any change to the lecture dates will be announced in class and may be accessed at http://pioneer.netserv.
chula.ac.th/~achairat. Students are strongly advised to attend lectures and participate in class, for attendance will be checked randomly and be used partially to adjust grade distribution. Since lecture materials involve mathematical statistics and economic intuitions and thus may be difficult at times, please attend classes punctually and read reading assignments beforehand. Attendance is highly recommended for tutorial sessions. They are designed to help you with homework assignments and to provide an additional avenue to learn econometrics. For computing assignments, you can utilize EVIEWS 4.1 available in our computer service center, room 316. My office is room number 519; phone number, 2218-6291 and 2218-6215; email, [email protected].
Below are course syllabus, outline and reading assignments, lecture and tutorial schedule.
Course Syllabus: Quantitative Methods of Economic Analysis
1. Course Number 2949605
2. Course Credit 3
3. Course Title Quantitative Methods in Economic
Analysis
4. Faculty / Department Faculty of Economics 5. Semester (First / Second / Summer) Trimester 1
6. Academic Year 2015
7. Instructor / Academic Staff Assistant Professor Chairat Aemkulwat, Ph.D.
8. Condition
8.1 Prerequisite 8.2 Corequisite 8.3 Concurrent
– – –
Students are assumed to have familiarity with basic calculus, probability, and mathematical statistics. Read Wooldridge (2009), Appendix A, B, C for those who have an inadequate background in these areas. Basic computer literary will be needed to complete the problem sets.
9. Status (Required / Elective) Required Course
10. Curriculum Master of Arts Program in Business and Managerial Economics
11. Degree Master of Arts
12. Hours / Week 12
13. Course Description
This course is intended to provide an introduction to regression analysis with cross- section and time-series data; topics include estimation, statistical inference, functional form, unit of measurement, asymptotics, prediction, dummy variables,
heteroskedasticity, serial correlation, weakly dependence and highly persistence.
14. Course Outline
14.1 Learning Content
30/9 1-2 Lecture 0 Intro to Econometrics Lecture 1 SLR
3/10 (Sa) 3-4 Lecture 2 Log and Unit Lecture 3 MRA Estimation 4/10 (Su) 5 Lecture 4 MLR Inference
Lecture 5 MLR Asymptotics 6/10 6-7 Lecture 6 Quadratic Interaction
Lecture 7 Prediction
26/10 8-9 Lecture 9 Heteroskedasticity
Midterm Examination
27/10 10 Lecture 10 Dummy Dependent Variable 2/11 11 Lecture 11 Basic Regressions
3/11 11 Lecture 11 Basic Regressions 8/11 (Su) 12 Lecture 12 Trends and Seasonality
9/11 13 Lecutre 13 Weakly Dependent 10/11 13 Lecutre 13 Weakly Dependent
16/11 14 Lecture 14 SC
17/11 14 Lecture 14 SC
Final Examination
14.2 Method
Lecture hour/time/period/ 70 percent
Lecture and discussion hour/time/period/ 20 percent
Brainstorming and discussion of case study so that students learn to analyze and solve problems
hour/time/period/ 10 percent
Making a summary of the main points or presentation of the results of researching or the assigned tasks
hour/time/period/percent)
–
14.3 Media
Powerpoint media 90 percent
Electronics and website media 5 percent
14.4 Assignment through Network System 14.5.1 Assigning and Submitting Method
Homework assignments can be obtained from my website, http://pioneer.netserv.chula.ac.th/~achairat
.
14.5.2 Learning Management System
Solutions to assigned exercises are given and lecture notes and pertinent announcements can be obtained from my website.
14.5 Evaluation
14.6.1 Assessment of academic knowledge 85 percent 14.6.2 Assessment of work or classroom activities –
14.6.3 Assessment of the assigned tasks 15 percent GRADING SYSTEM: Grade will be based on the following:
15 percent on homework assignments, 40 percent on the midterm examination and 45 percent on the final.
Grade distribution is as follows:
90-100 is A;
80-90, B+;
65-80, B;
55-65, C+;
45-55, C;
35-45, D+;
25-35, D;
below 25, F.
Note that 79.99 is B and 80.01 is B+. Students are strongly advised to attend lectures, for attendance will be checked randomly and be used partially to adjust grade distribution.
15. Reading List
15.1 Required Text
Wooldridge, Jeffrey, M., Introductory Econometrics: A Modern Approach, 5th International Edition (Canada: South-Western Cengage Learning), 2013.
15.2 Supplementary Texts
Eviews 4 User’s Guide, Quantitative Micro Software, 1994-2000.
Gujarati, D., Essentials of Econometrics (2e), McGraw-Hill, 2005.
Gujarati, D., Basic Econometrics (4e), McGraw-Hill, 2003
Johnston, J. and DiNardo, J., Econometric Methods (4e), McGraw-Hill, 1997.
Kennedy, P., A Guide to Econometrics, (3e), The MIT Press, 1994.
Pindyck, R. and Rubinfeld, L., Econometric Models and Economic Forecasts (4e),
McGraw-Hill, 1998.
Ramanathan, R. Introductory Econometrics with Applications, (5e), Thomson, 2001
Theil, H., Principles of Econometrics, John Wiley, 1976.
15.3 Research Articles / Academic Articles (If any) 15.4 Electronic Media or Websites
http://pioneer.netserv.chula.ac.th/~achairat
http://www.msu.edu/~ec/faculty/wooldridge/books.htm http://aise.swlearning.com
16. Teacher Evaluation
16.1 Which of the 12 types of teacher evaluation provided by the University is used in your class? If another form is used, please submit the form to The Quality
Assurance Division
02 Problem Based Learning 04 Lecture Learning
08 Lecture and Discussion 09 Tutorial Sessions
16.2 Changes made in accordance with the previous evaluation
The course has made adjustment in content and intended to be more participatory.
16.3 Discussion or analysis which creates desirable qualifications of Chulalongkorn University graduates
1) Academic Knowledge: Students will learn linear statistical techniques both in theory and economic and business application.
2) Professional Knowledge: Students will be able to understand papers involving regression analysis.
3) Ethics: Lectures and discussions will encourage students to have ethics in applying linear statistical techniques in their career.
4) Social Responsibility: Lectures, homework assignments, and punctuality will implant sense of responsibility and help understand his role in society.
OUTLINE AND READING ASSIGNMENTS I. Overview: Nature of Econometrics
Wooldridge, Chapter 1
Wooldridge, Appendix A, B, and C
II. Regression Analysis with Cross-Section Data 1. Simple Regression Model: Estimation
Wooldridge, Chapter 2
2. Logarithmic Functional Form and Units of Measurement Wooldridge, Appendix A.3-A.4; Chapter 2.4 and 6.1-6.2 3. Multiple Regression Analysis: Estimation
Estimation: Wooldridge, Chapter 3
Omitted Variable Bias: Wooldridge, Chapter 3.3 Multicollinearity: Wooldridge, Chapter 3.4
4. Inference: Hypothesis Testing and Confidence Interval Wooldridge, Chapter 4
5. OLS Asymptotoics: Estimation and Inference Wooldridge, Chapter 5
6. Further Issues in Multiple Regression Analysis
Quadratic and Interaction Terms: Wooldridge, Chapter 6.2 (Adjusted) R-Squared and Selection of Regressors, Chapter 6.3 7. Prediction
Wooldridge, Chapter 6.4
8. Dummy (Binary) Explanatory Variable Wooldridge, Chapter 7
9. Heteroskedasticity
Wooldridge, Chapter 8 10. Other Topics:
Dummy Dependent Variable: Wooldridge, Chapter 7.5 and 8.5 Functional Form Misspecification: Wooldridge, Chapter 9.1
III. Regression Analysis with Time-Series Data 1. Basic Time Series Regression Analysis
Wooldridge, Chapter 10
2. Weakly Dependent and Highly Persistent Time Series Wooldridge, Chapter 11 and 18.2
3. Serial Correlation and Heteroskedasticity Wooldridge, Chapter 12
,
Tentative Lecture schedule for PT15 in 2015
MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY
28 29 30 1 2 3 4
18:00-20:30 QMEA
9:00-16:00 QMEA
9:00-15:00 QMEA
5 6 7 8 9 10 11
18:00-20:30 QMEA
10:30-12:00 Tutor: Eviews 13:00-15:30 Tutor: HW1
12 3 14 15 16 17 18
9:30-12:00 Tutor: HW2
19 20 21 23 24 25
Chulalongkorn Day
9:30-12:00 Tutor: HW3
26 27 28 29 30 31
18:00-20:30 QMEA
18:00-20:30
QMEA 10:30-15:00
Tutor Midterm
MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY 1
2 3 4 5 6 7 8
18:00-20:30 QMEA
18:00-20:30 QMEA
9:00-12:00 Midterm
9:00-16:00 QMEA
9 10 11 12 13 14 15
18:00-20:30 QMEA
18:00-20:30 QMEA
9:30-12:00 Tutor: HW4
9:30-12:00 Tutor: HW5
16 17 18 19 20 21 22
18:00-20:30 QMEA
18:00-20:30 QMEA
9:30-12:00
Tutor: HW6 10:00-15:00 Tutor Final
23 24 25 26 27 28 29
9:00-12:00 Final
30
