• A
  • A
  • A
  • АБB
  • АБB
  • АБB
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта
2021/2022

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»
Статус: Дисциплина общефакультетского пула
Когда читается: 3 модуль
Охват аудитории: для всех кампусов НИУ ВШЭ
Преподаватели: Кускова Валентина Викторовна
Язык: английский
Кредиты: 4
Контактные часы: 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

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

Interim Assessment

  • 2021/2022 3rd module
    0.5 * Final In-Class or Take-home exam (at the discretion of the instructor) + 0.1 * Quizzes (Best 9 of 10, Varied points) + 0.2 * Homework Assignments (5 x Varied points) + 0.2 * In-Class Labs (9-10 x Varied points)
Bibliography

Bibliography

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