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Course title |
Decision Theory in Econometrics |
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Semester |
112-2 |
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Designated for |
COLLEGE OF SOCIAL SCIENCES DEPARTMENT OF ECONOMICS |
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Instructor |
YU-CHANG CHEN |
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Curriculum Number |
ECON5202 |
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Curriculum Identity Number |
323EU4250 |
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Class |
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Credits |
3.0 |
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Full/Half
Yr. |
Half |
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Required/
Elective |
Elective |
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Time |
Tuesday 6,7,8(13:20~16:20) |
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Remarks |
Restriction: juniors and beyond OR Restriction: MA students and beyond OR Restriction: Ph. D students
The upper limit of the number of students: 60. |
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Course introduction video |
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Table of Core Capabilities and Curriculum Planning |
Table of Core Capabilities and Curriculum Planning |
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Course Syllabus
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Please respect the intellectual property rights of others and do not copy any of the course information without permission
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Course Description |
See
https://docs.google.com/document/d/1ajX-IitOkNjAJ81AmgsS7sCoQpYYHpV5qvjmZKV8TCU/edit?usp=sharing
more details.
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Econ 5202 is designed to explore the intricate relationship between decision theory and data analysis, providing you with the tools and knowledge to address complex decision problems using econometric principles. We begin with engaging, real-world examples to ground our exploration in practical scenarios, setting the stage for a deeper understanding of decision-making in various economic contexts.
We will then continue our study with fundamental decision problems in econometrics, where we'll learn to rigorously define and analyze these problems using data. We will delve into critical areas of econometrics, including hypothesis testing, classification, prediction, and model selection. The course will culminate in an in-depth examination of the personalized treatment rule problem, offering a hands-on approach to theoretical concepts.
This course is an advanced course in the economics department. I expect that you are familiar with second-year microeconomics concepts such as utility maximization and choice under uncertainty. A solid background in introductory statistics and econometrics, including hypothesis testing and linear regression, is also required. This foundation will be crucial as we engage with the rigorous analysis of decision problems. That being said, I will consistently provide everyday examples to make the complex theories relatable and understandable. This approach aims to demonstrate the practicality and applicability of our learning, connecting theoretical econometrics to real-world economic decision-making.
A key component of this course is the final project, where you will apply what you've learned by framing and attempting to solve a decision theory problem of your choosing. This project will be a space for creativity and practical application, with regular class discussions to refine your ideas. I will set structured milestones to guide your progress and ensure a comprehensive learning experience.
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Course Objective |
1. To equip students with a solid foundation in decision theory as it applies to econometrics, emphasizing both theoretical concepts and practical applications. This includes an understanding of the fundamental principles of decision-making under uncertainty and how these principles guide data-driven decision processes.
2. To develop students' analytical skills in identifying, framing, and solving decision problems using econometric methods. This includes the ability to apply statistical theories and models to real-world scenarios, enhancing their problem-solving and critical-thinking capabilities. |
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Course Requirement |
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Student Workload (expected study time outside of class per week) |
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Office Hours |
Appointment required. |
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Designated reading |
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References |
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Grading |
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Adjustment methods for students |
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Teaching methods |
Assisted by video |
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Assignment submission methods |
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Exam methods |
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Others |
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