Магистратура
2025/2026
Анализ временных рядов
Статус:
Курс по выбору (Финансы)
Кто читает:
Банковский институт
Где читается:
Банковский институт
Когда читается:
1-й курс, 3 модуль
Онлайн-часы:
20
Охват аудитории:
для своего кампуса
Преподаватели:
Липин Андрей Станиславович
Язык:
английский
Кредиты:
3
Контактные часы:
8
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.
- Students 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.
- 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
- Understand trend-seasonal decomposition
- Understand the ETS model and theta-model
- Know how to do Box-Cox transformation
- Build the ACF and PACF
- Interpret the ARIMA models
- Conduct stationarity tests
- Know how to create predictors
- Know the difference between the ARIMAX and ARDL model
- Learn how to compare models
- Learn how to handle missing data
- Know how to detect anomalies
- Learn about structural breaks
Course Contents
- Trend-seasonal decomposition and exponential smoothing models
- ARIMA models
- Time series forecasting
- Pre-procssing data
Assessment Elements
- Test 1The online tests consist of multiple-choice questions and numerical answer questions. Some tasks require basic computations or coding. All course materials and examples use R. Students are expected to be able to interpret and, if needed, reproduce results using R. Calculations may be performed using other tools at the student’s discretion; however, final answers must be submitted in the required format. Online tests are evaluated automatically.
- Test 2The online tests consist of multiple-choice questions and numerical answer questions. Some tasks require basic computations or coding. All course materials and examples use R. Students are expected to be able to interpret and, if needed, reproduce results using R. Calculations may be performed using other tools at the student’s discretion; however, final answers must be submitted in the required format. Online tests are evaluated automatically.
- ProjectThe final project consists of individual applied work with a real economic or financial time series. The objective of the project is to compare forecasting models based on their predictive performance and to demonstrate an understanding of how data preprocessing and model choice affect forecast quality. The project is individual, submitted electronically, and assessed by the lecturer.
Bibliography
Recommended Core Bibliography
- Banerjee, A., Dolado, J. J., Galbraith, J. W., & Hendry, D. (1993). Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data. Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.oxp.obooks.9780198288107
- 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
- Tsay, R. S. (2010). Analysis of Financial Time Series (Vol. 3rd ed). Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=334288
Recommended Additional Bibliography
- Mills, T. C., & Markellos, R. N. (2008). The Econometric Modelling of Financial Time Series: Vol. 3rd ed. Cambridge University Press.