Course Information
Course title
Computational Methods for Econometrics 
Semester
109-2 
Designated for
COLLEGE OF SOCIAL SCIENCES  GRADUATE INSTITUTE OF ECONOMICS  
Instructor
CHIH-SHENG HSIEH 
Curriculum Number
ECON7218 
Curriculum Identity Number
323EM3770 
Class
 
Credits
3.0 
Full/Half
Yr.
Half 
Required/
Elective
Elective 
Time
Monday 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: 25. 
 
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

In modern economic research, computers enhance our capacity of analyzing complex problems with data support. Computation is particularly important in fields involving dynamic modeling, structural equations, and massive data, such as macro, labor, and industrial organization. However, computational methods have not been part of the core curriculum of postgraduate-level economics education, whereas programming skills are critical for a postgraduate’s success in academia and industry. The objective of this course is to introduce graduate students to commonly applied computational approaches for solving econometric models, with an emphasis on numerical optimization, Bayesian MCMC, simulation-based methods, and dynamic structural model estimation. We expect that at the end of the course a student would proficiently use at least one programming language (Stata, Matlab, R, etc.). Moreover, we aim to equip the students with the computational ability to tackle problems of their own research areas.
 

Course Objective
This course intends to introduce students with computational methods for solving econometric problems, and expose students to extensive programming exercises. After completing this course, students should
1. have intermediate skills on using STATA, R, and MATLAB.
2. be familiar with well-known computational methods used in the current literature
3. be able to explore and potentially solve computational challenges faced by their own research.
 
Course Requirement
1. Course assignments 50%
There will be several assignments to exercise the problem solving and software coding.

2. presentation 50%
Students should give a presentation of one journal article chosen from the reading list.
 
Student Workload (expected study time outside of class per week)
 
Office Hours
Mon. 13:30~15:00 
Designated reading
 
References
 
Grading
   
Progress
Week
Date
Topic
No data