Bachelor
2024/2025
Time Series Analysis
Type:
Compulsory course (Economics)
Area of studies:
Economics
Delivered by:
School of Economics and Finance
Where:
Faculty of Management
When:
4 year, 1, 2 module
Mode of studies:
distance learning
Online hours:
28
Open to:
students of one campus
Instructors:
Evgeniya Shenkman
Language:
English
ECTS credits:
5
Course Syllabus
Abstract
The course aims to provide students with a theoretical understanding of the basics of time series modeling and demonstrate their application on real data. This course is blended. Students learn theory from the online course. On practical session, they will apply models to macroeconomic and financial data. The course begins with essentials of working with time series data. The next part of the course covers all basic time series models, such as: ARIMA, SARIMA, ARCH and GARCH, VAR and VECM. As a result of the course, student will make a project on real data: prepare data for analysis, choose appropriate model, apply it and interpret results. The practical session R language is applied.
Learning Objectives
- Analyze economic data in accordance with the task, make preliminary data analysis.
- Build appropriate econometric time series models for the research question, analyze and interpret results.
- Understand limitation and relevance of the models.
Expected Learning Outcomes
- Know basic concepts of multivariate time series analysis, build appropriate econometric time series models.
- Know basic concepts of univariate time series analysis, build appropriate econometric time series models.
Assessment Elements
- Test
- ReportsAfter each seminar session student prepare report of performed tasks, the tasks are partly done in the class, partly at home
- ExamThe exam is a project that can be done in the groups with no more than 2 students. The students do data analysis on time series and write a report.
Bibliography
Recommended Core Bibliography
- Chatfield, C., & Xing, H. (2019). The Analysis of Time Series : An Introduction with R (Vol. Seventh edition). Boca Raton, Florida: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2110461
- Levendis, J. D. (2018). Time Series Econometrics : Learning Through Replication. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2016053
- Palma, W. (2016). Time Series Analysis. Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1229817
Recommended Additional Bibliography
- Bleikh, H. Y., & Young, W. (2013). Time Series Analysis and Adjustment : Measuring, Modelling and Forecasting for Business and Economics. Farnham: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=531761
- Klaus Neusser. (2016). Time Series Econometrics. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sptbec.978.3.319.32862.1
- Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Berlin: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=145686
- Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting (Vol. Second edition). Hoboken, New Jersey: Wiley-Interscience. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=985114
- Tsay, R. S. (2013). Multivariate Time Series Analysis : With R and Financial Applications. Wiley.