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# Machine Learning in Economics

2020/2021
Учебный год
ENG
Обучение ведется на английском языке
6
Кредиты
Статус:
Курс по выбору
Когда читается:
4-й курс, 2, 3 модуль

#### Преподаватели

Дуплинский Артем Александрович

Фролова Виктория Александровна

### 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

• Students 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

• Understand the concept of data generating process and how it is different to the concept of model
• Learn more details on hypotheses testing and concepts like stationarity, and ergodicity

#### Course Contents

• Opening and Intro to TS concepts
• Probability Models and Data Generating Processes
• Presentations and Questions
• Asymptotic Results for Unit-root processes

• Exam
• presentation
• assignment

#### Interim Assessment

• Interim assessment (3 module)
0.18 * assignment + 0.7 * Exam + 0.12 * presentation

#### 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