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Обычная версия сайта
2023/2024

# Анализ временных рядов

Статус: Маго-лего
Когда читается: 3 модуль
Охват аудитории: для своего кампуса
Язык: английский
Кредиты: 3
Контактные часы: 28

### Course Syllabus

#### Abstract

Time Series Analysis (Master level) is an elective course designed for the first year Master students of “Finantial Analytic” Program. This is an intermediate course of Time Series Theory for the students specializing in the field of Finance and Banking. The course is taught in English.The stress in the course is made on the sense of facts and methods of time series analysis. Conclusions and proofs are given for some basic formulas and models; this enables the students to understand the principles of economic theory. The main stress is made on the economic interpretation and applications of considered economic models.

#### Learning Objectives

• The students should get acquainted with the main concepts of Time Series theory and methods of analysis. They should know how to use them in examining financial processes and should understand methods, ideas, results and conclusions that can be met in the majority of books and articles on economics and finance. In this course, students should master traditional methods of Time Series analysis, intended mainly for working with time series data. Students should understand the differences between cross-sections and time series, and those specific economic problems, which occur while working with data of these types

#### Expected Learning Outcomes

• Students should become skillful in analysis and modelling of stochastic processes of ARMA (p, d, q) models, get acquainted with co-integration and error correction models, autoregressive models with distributed lags, understand their application in economics. Considered methods and models should be mastered by practice using real economic data and modern economic software Econometric views and R

#### Course Contents

• Stochastic process and its main characteristics
• Autoregressive-moving average models ARMA (p,q). Estimation of coefficients of ARMA (p,q) processes. Box-Jenkins’ approach
• Forecasting in Box-Jenkins model
• Non-stationary time series
• Unit root problems. Unit root and structure changes
• Regressive dynamic models
• Vector autoregressive model and co-integration
• Causality in time series

#### Assessment Elements

• home assignment
• exam

#### Interim Assessment

• 2023/2024 3rd module
0.5 * exam + 0.5 * home assignment

#### Recommended Core Bibliography

• Enders, W. (2015). Applied Econometric Time Series (Vol. Fourth edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1639192