Магистратура
2025/2026
Применение машинного обучения в экономике
Статус:
Курс обязательный (Аналитика данных для бизнеса и экономики)
Кто читает:
Департамент экономики
Где читается:
Санкт-Петербургская школа экономики и менеджмента
Когда читается:
2-й курс, 1, 2 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Сысоев Дмитрий Сергеевич
Язык:
английский
Кредиты:
6
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
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