Delivered at:: School of Economics and Finance

Course type:: Elective course

When:: 4 year, 1, 2 module

Instructor

Shenkman, Evgeniya

Full 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.

Course Contents

Univariate time series analysis
Mutivariate time series analysis

Assessment Elements

Test
Exam
The 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.
Reports
After each seminar session student prepare report of performed tasks, the tasks are partly done in the class, partly at home

Interim Assessment

2023/2024 2nd module
0.3 * Exam + 0.2 * Reports + 0.2 * Reports + 0.3 * Test

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.

Authors

Shenkman Evgeniia Andreevna

Bachelor’s Programme 'Economics'

Time Series Analysis