Course Information
Course title
Econometric Theory (Ⅰ) 
Semester
110-1 
Designated for
COLLEGE OF SOCIAL SCIENCES  GRADUATE INSTITUTE OF ECONOMICS  
Instructor
CHIH-SHENG HSIEH 
Curriculum Number
ECON7026 
Curriculum Identity Number
323EM0650 
Class
 
Credits
4.0 
Full/Half
Yr.
Half 
Required/
Elective
Required 
Time
Monday 9,10(16:30~18:20) Thursday 2,3,4(9:10~12:10) 
Remarks
Restriction: MA students and beyond OR Restriction: Ph. D students
The upper limit of the number of students: 60. 
Ceiba Web Server
http://ceiba.ntu.edu.tw/1101ECON7026_ 
Course introduction video
 
Table of Core Capabilities and Curriculum Planning
Table of Core Capabilities and Curriculum Planning
Course Syllabus
Please respect the intellectual property rights of others and do not copy any of the course information without permission
Course Description

This is a graduate level Econometrics course designed for Master/Ph.D. students of Economics and other Business majors. The first goal of this course is to deepen our knowledge on the theoretical properties of widely used econometric estimation methods, including least square, maximum likelihood, and generalized method of moments. We start this course with the linear regression models and then move to more general nonlinear models. The second goal is to enhance students' programming skills. There are problem sets for students to use STATA and R (or MATLAB, Python) to implement empirical and simulation exercises.  

Course Objective
After finishing this course, students are expected to
1. have a comprehensive understanding of regression methods.
2. acquire enough knowledge to read academic papers or advanced econometric textbooks.
3. equip with programming skills in STATA, R, or MATLAB.
4. be capable of conducting empirical analysis on economic and financial data.
 
Course Requirement
It is presumed that students have studied undergraduate Econometrics and are familiar with the basic regression analysis.  
Student Workload (expected study time outside of class per week)
 
Office Hours
Mon. 13:30~15:00 Note: or make appointment 
Designated reading
Hansen, B. E. (2021) Econometrics
http://www.ssc.wisc.edu/~bhansen/econometrics/Econometrics.pdf
 
References
Econometric Analysis (7th Ed), Greene, Prentice-Hall, 2011
Microeconometrics: Methods and Applications, Cameron and Trivedi, Cambridge, 2005
Econometric Analysis of Cross Section and Panel Data, Wooldridge, MIT, 2002

For undergraduate-level knowledge:
Introductory Econometrics: A Modern Approach. J. M. Wooldridge, Cengage, 2012
Introduction to Econometrics (3rd Ed), Stock and Watson, Addison Wesley, 2010
 
Grading
 
No.
Item
%
Explanations for the conditions
1. 
Final Exam 
35% 
Final exam is cumulative, but mainly focuses on materials after the midterm exam. 
2. 
Midterm Exam 
35% 
 
3. 
Problem Sets 
30% 
 
 
Progress
Week
Date
Topic
Week 1
9/23  Statistical Reviews  
Week 2
9/30  Regression Analysis  
Week 3
10/07  Least Square Method and Gauss Markov Theorem  
Week 4
10/14  Least Square Method and Gauss Markov Theorem (continued)  
Week 5
10/21  Violation of Gauss Markov Assumptions  
Week 6
10/28  Violation of Gauss Markov Assumptions (continued) 
Week 7
11/04  Distribution Assumptions and Maximum Likelihood  
Week 8
11/11  Distribution Assumptions and Maximum Likelihood(continued)  
Week 9
11/18  An Introduction to Large Sample Asymptotics 
Week 10
11/25  Asymptotic Theory for Least Squares 
Week 11
12/02  Restricted Estimation 
Week 12
12/09  Restricted Estimation (continued) 
Week 13
12/16  Hypothesis Testing 
Week 14
12/23  Hypothesis Testing (continued) 
Week 15
12/30  Resampling Methods 
Week 16
01/06  Resampling Methods (continued)