Chulalongkorn University: Master of Arts Program in Business and Managerial Economics Economics 2949605 Quantitative Methods in Economic Analysis Chairat Aemkulwat 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, chairat.a@chula.ac.th. 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 1 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 crosssection 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 3/10 (Sa) 3-4 4/10 (Su) 5 6/10 6-7 26/10 8-9 27/10 2/11 3/11 8/11 (Su) 9/11 10/11 16/11 17/11 10 11 11 12 13 13 14 14 Lecture 0 Intro to Econometrics Lecture 1 SLR Lecture 2 Log and Unit Lecture 3 MRA Estimation Lecture 4 MLR Inference Lecture 5 MLR Asymptotics Lecture 6 Quadratic Interaction Lecture 7 Prediction Lecture 9 Heteroskedasticity Midterm Examination Lecture 10 Dummy Dependent Variable Lecture 11 Basic Regressions Lecture 11 Basic Regressions Lecture 12 Trends and Seasonality Lecutre 13 Weakly Dependent Lecutre 13 Weakly Dependent Lecture 14 SC Lecture 14 SC Final Examination 14.2 Method Lecture Lecture and discussion Brainstorming and discussion of case study so that students learn to analyze and solve problems 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 Visualizer media - opaque sheets hour/time/period/ 70 percent hour/time/period/ 20 percent hour/time/period/ 10 percent – 5 percent 2 Powerpoint media Electronics and website media 90 percent 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), 3 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. 4 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 , 5 Tentative Lecture schedule for PT15 in 2015 October MONDAY 28 5 TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY 29 30 18:00-20:30 QMEA 1 2 3 9:00-16:00 QMEA 4 9:00-15:00 QMEA 6 7 8 9 10 11 18:00-20:30 QMEA 12 3 10:30-12:00 Tutor: Eviews 13:00-15:30 Tutor: HW1 14 15 16 17 18 9:30-12:00 Tutor: HW2 19 26 18:00-20:30 QMEA 20 27 18:00-20:30 QMEA 21 28 23 24 Chulalongkorn Day 9:30-12:00 Tutor: HW3 30 31 29 25 10:30-15:00 Tutor Midterm November MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY 1 2 3 18:00-20:30 QMEA 18:00-20:30 QMEA 9 10 18:00-20:30 QMEA 18:00-20:30 QMEA 16 17 18:00-20:30 QMEA 18:00-20:30 QMEA 23 24 4 11 18 5 12 19 6 13 20 7 8 9:00-12:00 Midterm 9:00-16:00 QMEA 14 15 9:30-12:00 Tutor: HW4 9:30-12:00 Tutor: HW5 21 22 9:30-12:00 Tutor: HW6 25 26 27 28 9:00-12:00 Final 30 6 10:00-15:00 Tutor Final 29 7