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.