Time Series Analysis

2024/2025

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered at:

International Laboratory for Applied Network Research

Course type:

Elective course

When:

2 year, 1, 2 module

Instructor

Yakovleva, Dina

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

Assessment Elements

• Homework Assignment
• Homework assignments

Interim Assessment

• 2024/2025 2nd module
  0.5 * Homework Assignment + 0.5 * Homework assignments

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

Authors

• Pavlova Irina Anatolevna
• Klimov Ivan Aleksandrovich
• BOLDYREVA LYUBOV VLADIMIROVNA