This course is designed for the M.Sc. Programme in Mathematics. This course is focused on the importation of mathematical and statistical concepts, theories, methods and limitations into economic and data science and provides an overview of econometric methods and data analysis that allows analysts to better understand their economic/business landscape and to improve their ability to make sound economic/business forecasts. Through hands-on exercises based on the information flow of sample data together with available computing software, e.g., MATLAB, Python and/or R programming languages, participants gain knowledge of the practical elements of applied econometric analysis. The overall aim is to sharpen the quantitative, numerical, statistical and analytical skills of participants in dealing with real world examples and issues related to business and economic models.
This course assumes no prior experience with programming.
M. Verbeek, A Guide to Modern Econometrics, John Wiley & Sons, 2017.
S. Hubbert, Essential mathematics for market risk management, John Wiley & Sons, 2012.
The text/references is/are available at the CUHK library.
The text/reference should not be treated as a substitute for the lectures. The lectures may present the material covered in the text in a different manner, or deviate from it entirely. You should take your own notes in class.
The Chinese University of Hong Kong places very high importance on honesty in academic work submitted by students, and adopts a policy of zero tolerance on cheating and plagiarism. Any related offence will lead to disciplinary action including termination of studies at the University. For information on categories of offenses and types of penalties, students should consult the following link: .
Your final letter-grade will be determined by the criterion-referenced assessment.
Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday |
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1 | 2 | 3 | 4 | 5 | 6 | |
7 | 8: Lecture 1 | 9 | 10 | 11 | 12 | 13 |
14 | 15: Lecture 2 | 16 | 17 | 18 | 19 | 20 |
21 | 22: Lecture 3 + Lab 1 | 23 | 24 | 25 | 26 | 27 |
28 | 29: Lecture 4 | 30 | 31 |
Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday |
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1 | 2 | 3 | ||||
4 | 5: Lecture 5 + Lab 2 | 6 | 7 | 8 | 9 | 10 |
11 | 12 | 13 | 14 | 15 | 16 | 17 |
18 | 19: Lecture 6 | 20 | 21 | 22 | 23 | 24 |
25 | 26: Lecture 7 + Midterm | 27 | 28 | 29 |
Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday |
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1 | 2 | |||||
3 | 4: Lecture 8 | 5 | 6 | 7 | 8 | 9 |
10 | 11: Lecture 9 + Lab 3 | 12 | 13 | 14 | 15 | 16 |
17 | 18: Lecture 10 | 19 | 20 | 21 | 22 | 23 |
24 | 25: Lecture 11 + Lab 4 | 26 | 27 | 28 | 29 | 30 |
31 |
Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday |
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1 | 2 | 3 | 4 | 5 | 6 | |
7 | 8: Lecture 12 | 9 | 10 | 11 | 12 | 13 |
14 | 15: Review + Lab 5 | 16 | 17 | 18 | 19 | 20 |
21 | 22 Final | 23 | 24 | 25 | 26 | 27 |
28 | 29 | 30 |
There will be three graded homework assignments.
Please note that you MUST do the whole homework entirely by yourself. In case of difficulty, you may consult the instructor and the tutors during their office hours. Any answers that show evidence of having been done with others will receive a score of zero; stronger action may also be taken (visit ). Don’t copy the work of others! Be neat, concise and well-organized.
Late homework answers will NOT be graded, and will receive a score of zero.
Once you have enrolled your course, we will send you a username and password to access your online learning resources.