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.
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
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