Master
2024/2025
Time Series Analysis
Type:
Elective course (Data Analytics and Social Statistics)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
International laboratory for Applied Network Research
Where:
Faculty of Social Sciences
When:
2 year, 1, 2 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Dina Yakovleva
Master’s programme:
Applied Statistics with Network Analysis
Language:
English
ECTS credits:
6
Contact hours:
40
Course Syllabus
Abstract
This course is about quantitative methods, namely statistics, applied to social sciences. Specifically, we will focus on certain statistical competencies that help evaluate processes over time. I expect you to understand the basics of statistics you’ve learned previously in this course; everything else we will learn in this class. As you will see, we will use a lot of real-world datasets, and I am concerned more with your understanding on how statistic works as opposed to memorizing the formulas. This class will be unique in a sense that I will bring a lot of non-statistical material to help you understand the world of decision sciences.
Learning Objectives
- The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.
Expected Learning Outcomes
- Be able to present and/or interpret data in tables and charts.
- Be able to understand and apply descriptive statistical measures to real-life situations.
- Be able to understand and apply probability distributions to model different types of social processes.
- Be able to understand the meaning and use of longitudinal models.
- Have an ability to forecast future numbers based on historical data.
- Have an ability to resolve problems and recognize the most common decision errors and make tough decisions in a competent way.
- Have an ability to use computer software to perform statistical analysis on data (specifically, STATA).
- Know modern applications of longitudinal analysis.
- Know the theoretical foundation of longitudinal analysis.
- Know the variety of time-series models that are available to analyze real-life problems, starting with the simple OLS regression and ending with highly advanced models.
Course Contents
- Introduction to the Framework of longitudinal data analysis
- Basics of Time Series I
- Basics of Time Series II
- ARIMA
- Advanced time-series models I
- Advanced time-series models II
- Advanced time-series models III
- Advanced time-series models IV
Bibliography
Recommended Core Bibliography
- 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
- Taris, T. (2000). A Primer in Longitudinal Data Analysis. London: SAGE Publications Ltd. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=251795
- 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
- Beran, J. (2017). Mathematical Foundations of Time Series Analysis : A Concise Introduction. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1741935
- Franses, P. H., & Paap, R. (2004). Periodic Time Series Models. Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.oxp.obooks.9780199242030
- 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