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Магистратура 2019/2020

Анализ лонгитюдных данных

Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Лучший по критерию «Новизна полученных знаний»
Направление: 01.04.02. Прикладная математика и информатика
Когда читается: 2-й курс, 3 модуль
Формат изучения: без онлайн-курса
Прогр. обучения: Прикладная статистика с методами сетевого анализа
Язык: английский
Кредиты: 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