Machine Learning in Economics

2021/2022

Academic Year

ENG

Instruction in English

5

ECTS credits

Delivered at:

Department of Economics

Course type:

Elective course

When:

4 year, 2 module

Instructors

Багиров Фарид Вугар Оглы

Ivanov, Dmitry

Кучерявый Константин Сергеевич

Course Syllabus

Abstract

Economists use time-series methods in many circumstances. They estimate economic models, build policy analyses and forecast economic variables. In this course we will cover some crucial concepts to establish a solid background for diving deeper in the world of time-series econometrics. For some of the methods we will go into details to learn why and how they work. We will revisit concepts like stationarity, consistency, asymptotic normality.

Learning Objectives

• tudents will feel comfortable orienting among different statistical methods and develop a feeling of why these methods work and how to extend them

Expected Learning Outcomes

• Learn more details on hypotheses testing and concepts like stationarity, and ergodicity
• Understand different methods for supervised learning such as linear regression, logistic regression, classification tools
• Understand different methods for unsupervised learning such as principal component analysis, k-means clustering
• Understand the concept of data generating process and how it is different to the concept of model

Course Contents

• Opening and Intro to TS concepts
• Probability Models and Data Generating Processes
• Practical differences between machine learning and statistical approaches
• Presentations and Questions

Assessment Elements

• assignment
• Final project
  Some groups might be asked to defend their final assignment (either randomly picked or in case there are some doubts in how genuine the assignment is). In particular, note that it’s ok if one person focused more on one exercise than another. However, it’s expected that all of you understood how each of the exercises were solved. If your teammate solved the exercise and during the defense you are asked the question about it, it is your responsibility to give an answer. If you don’t know – your grade might be lowered. Plagiarism in the assignment You are allowed to use different sources when completing your assignment. However, you have to answer questions in your own words and in your own code. Plagiarism cases will be punished by a fail.
• presentation

Interim Assessment

• 2021/2022 2nd module
  0.7 * Final project + 0.12 * presentation + 0.18 * assignment

Bibliography

Recommended Core Bibliography

• Brockwell, P. J., & Davis, R. A. (2002). Introduction to Time Series and Forecasting (Vol. 2nd ed). New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=108031

Recommended Additional Bibliography

• Ragnar Nymoen. (2019). Dynamic Econometrics for Empirical Macroeconomic Modelling. World Scientific Publishing Co. Pte. Ltd. https://doi.org/10.1142/11479