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Regular version of the site
Master 2019/2020

Longitudinal Data Analysis

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Area of studies: Applied Mathematics and Informatics
When: 2 year, 3 module
Mode of studies: offline
Instructors: Valentina Kuskova
Master’s programme: Applied Statistics with Network Analysis
Language: English
ECTS credits: 4

Course Syllabus


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

  • Know the theoretical foundation of longitudinal analysis.
  • Be able to understand the meaning and use of longitudinal models.
  • Know modern applications 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.
  • Be able to present and/or interpret data in tables and charts.
  • Have an ability to use computer software to perform statistical analysis on data (specifically, STATA).
  • 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.
  • 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.
Course Contents

Course Contents

  • Introduction to the Framework of longitudinal data analysis
    The Where, Why, and How of Longitudinal Data. Simple Linear Regression Model – A Review
  • Basics of Time Series I
    Basics of Time Series Analysis. Static and Finite Distributed Lag models.
  • Basics of Time Series II
    Trending, non-stationarity, serial correlation. Autoregressive (AR) proves and moving average (MA) process.
    Autoregressive integrated moving average model (ARIMA) with extensions. Box-Jenkins meth-od for working with ARIMA.
  • Advanced time-series models I
    Cointegration. Equilibrium. Engle-Granger two-step procedure. Error correction models (ECM) and vector autoregression models (VAR). Reduced form VAR. Lag length selection and infor-mation criterion.
  • Advanced time-series models II
    Structural vector autoregression models, including short-run (SVAR). Long-run restrictions. Structural equation models (SEM). The state-space approach to time series analysis. Predicted states, filtered states, smoothed states, forecasting.
  • Advanced time-series models III
    Time-series with categorical predictors. Binary response. Random vs. fixed effects. Mixed model assumptions and estimation. Non-linear mixed effects. Observed marginal proportions, proportional and non-proportional odds.
  • Advanced time-series models IV
    Panel and time series cross-sectional data (TSCS). Benefits of time-space data. Variable interceps and slopes. Errors in the TSCS models. Heterogeneity and pooling. Fixed and random effects estimation.
Assessment Elements

Assessment Elements

  • non-blocking Final In-Class or Take-home exam (at the discretion of the instructor)
  • non-blocking Quizzes (Best 9 of 10, Varied points)
  • non-blocking In-Class Labs (9-10 x Varied points)
  • non-blocking Homework Assignments (5 x Varied points)
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.5 * Final In-Class or Take-home exam (at the discretion of the instructor) + 0.2 * Homework Assignments (5 x Varied points) + 0.2 * In-Class Labs (9-10 x Varied points) + 0.1 * Quizzes (Best 9 of 10, Varied points)


Recommended Core Bibliography

  • Analysis of financial time series, Tsay, R. S., 2005
  • Derryberry, D. R. (2014). Basic Data Analysis for Time Series with R. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=817454
  • 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

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