• A
• A
• A
• ABC
• ABC
• ABC
• А
• А
• А
• А
• А
Regular version of the site
Master 2020/2021

## Longitudinal Data Analysis

Area of studies: Applied Mathematics and Informatics
When: 1 year, 3 module
Mode of studies: offline
Open to: students of all HSE University campuses
Instructors: Valentina Kuskova
Master’s programme: Applied Statistics with Network Analysis
Language: English
ECTS credits: 8

### 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.
• 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

• 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

• 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.
• ARIMA
Autoregressive integrated moving average model (ARIMA) with extensions. Box-Jenkins meth-od for working with ARIMA.
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.
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.
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.
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

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

#### 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