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Course Syllabus

2025/2026

Анализ временных рядов

Статус: Маго-лего
Кто читает: Международная лаборатория прикладного сетевого анализа
Где читается: Международная лаборатория прикладного сетевого анализа
Когда читается: 1, 2 модуль
Охват аудитории: для своего кампуса
Преподаватели: Яковлева Дина Сергеевна
Язык: английский
Контактные часы: 40

Course Syllabus

Full SyllabusAsk Question

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

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

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

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

Assessment Elements

  • non-blocking Lab 2 (FDL models)
    Mandatory task to fulfill You are allowed to work in pairs (2 students max)
  • non-blocking FDL to ARIMA
    One of topics to choose to fulfill four tasks in total (topic is up to students' choice) You are allowed to work in pairs (2 students max)
  • non-blocking Box Jenkins
    One of topics to choose to fulfill four tasks in total (topic is up to students' choice) You are allowed to work in pairs (2 students max)
  • non-blocking ECM VAR
    One of topics to choose to fulfill four tasks in total (topic is up to students' choice) You are allowed to work in pairs (2 students max)
  • non-blocking SVAR STEM SPACE
    One of topics to choose to fulfill four tasks in total (topic is up to students' choice) You are allowed to work in pairs (2 students max)
  • non-blocking Panel and Pooled Cross Sectional data
    One of topics to choose to fulfill four tasks in total (topic is up to students' choice) You are allowed to work in pairs (2 students max)
  • non-blocking Filtering
    One of topics to choose to fulfill four tasks in total (topic is up to students' choice)
  • non-blocking Time-Series with cathegorical+Forecasting (in 1 block)
    One of topics to choose to fulfill four tasks in total (topic is up to students' choice) You are allowed to work in pairs (2 students max)
  • non-blocking Final project
    You are allowed to work in pairs (2 students max)
Interim Assessment

Interim Assessment

  • 2025/2026 2nd module
    60% of grade is homework grade, where 5 homework tasks should be passed (1 mandatory - FDL topic + any 4 topics of listed ones) 40% of grade is Final exam grade
Bibliography

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

  • YAKOVLEVA DINA SERGEEVNA
  • Klimov Ivan Aleksandrovich
  • Pavlova Irina Anatolevna