Econometrics and Machine Learning (MSB145)

In the increasingly data-driven business environment, it is crucial for a modern econ and finance graduate to know how to use data. This course offers students a comprehensive exploration of the fundamental principles of econometrics, with a specific focus on their applications in finance and economics. By examining the core concepts of econometrics, students will gain a deep understanding of the strengths and limitations of each, enabling them to make informed choices when addressing real-world problems. In addition, students will be introduced to basic machine learning methods. At the end of the course, students will possess a versatile skill set, allowing them to navigate complex issues in finance and economics, making data-driven decisions while appreciating the nuances of both econometric and machine learning methodologies.

Examples of typical subject areas covered are:

• Causality

• Multiple Linear Regression

• Randomized Controlled Trials

• Quasi experimental methods

• Panel Data Estimation

• Time series

• Forecasting

• Machine Learning Methods

Knowledge

On completion of the course, students will gain knowledge in:

• Econometric Methods

• Basic Machine Learning Methods

• Programming in R

Skills

Upon completion of this course, students will be able to:

• Interpret the results of different econometric and machine learning methods.

• Implement econometric methods in new data analysis contexts.

• Compare and contrast different econometric and machine learning methods to answer a research question with data.

• Formulate a research question and analyze it with data and the methods learned using R.

• Show skills for written communication.

• Demonstrate abilities to communicate and work effectively with others.

This course uses a mixture of interactive lectures, TA sessions, and individual study. Lecture slides provide the basic

concepts. The material is explained and extended in the in-person lectures which also give room for student questions.

Programming and empirical exercises are discussed in the TA sessions.