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
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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 
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
 
Designated reading
 Hansen, B. E. (2021) Econometrics
http://www.ssc.wisc.edu/~bhansen/econometrics/Econometrics.pdf
 
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)