Panel Data: Analysis and Applications for the Social Sciences

2020/2021

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Кафедра высшей математики (Общеуниверситетские кафедры)

Статус:

Курс по выбору

Когда читается:

1-й курс, 3 модуль

Преподаватель

Сальникова Дарья Вячеславовна

Course Syllabus

Abstract

“Panel data: Analysis and Applications for the Social Sciences” is a blended-learning course. The online course “Getting and Cleaning Data” (https://www.coursera.org/learn/data-cleaning) covers the basics of data manipulation in R. The first part of the course gives an overview of multiple regression models. The second part of the course focuses on the methodological tools necessary to succeed in handling panel data, namely, regression models with interaction terms and exploratory longitudinal data analysis. The third part covers fixed-effects and random-effects models. Lectures provide students with the theoretical foundations of panel data analysis. Practical sessions develop data analysis and data visualization skills. Students use RStudio for statistical analysis. At the practical sessions, students discuss the key approaches to handling panel data and illustrate them with different examples from social science research, in particular, economic sociology. Students are given datasets from original studies to replicate the findings and change the model specifications if needed.

Learning Objectives

• The course aims to provide students with the theoretical background and practical skills in conducting panel data analysis. Specifically, the learning objectives are as follows: to enable students to choose appropriate models for panel data analysis; to develop data manipulation and visualization skills; to enable students to implement linear panel models in RStudio

Expected Learning Outcomes

• By the end of the course students are expected to apply fixed- and random- effects models to analyze panel data, to interpret the results, to have data visualization skills and skills in implementing the afore-mentioned methods by using RStudio in the context of panel data analysis. Students will learn the advantages and limitations of different approaches to panel data analysis. This knowledge will help students choose a set of appropriate statistical tools to test their research hypotheses.

Course Contents

• Introduction. Linear regression analysis
  Types of data structures. Multiple linear regression models with their applications to crosssectional data. Assumptions. Model specification. Interpretation of regression analysis results. Model diagnostics.
• Interaction terms in regression analysis
  Moderation VS Mediation. Conditional hypotheses with examples from social science research. Multiple linear regression models with interaction terms. Model specification. Interpretation of interaction effects. Interaction between binary predictors. Interaction between binary and continuous predictors. Marginal effects. Visualization of interaction effects.
• Data manipulation. Supplementary tools for panel data analysis
  Panel VS Time-series cross-section (TSCS) VS Time-series data. Exploratory data analysis and visualization of panel data. Within- and between-group variation. Reshaping data. Merging data. Students are required to listen to the following lectures online (Week 3, Week 4, “Getting and Cleaning Data”. Available at: https://www.coursera.org/learn/data-cleaning) before the given practical session.
• Fixed-effects models
  Fixed-effects model VS pooled model. Least-squares dummy-variable models. Within-group transformation. The technique underlying the estimation of coefficients in fixed-effects models. Aggregation bias. Model diagnostics.
• Random-effects models VS Fixed-effects models
  Random-effects models: assumptions, model estimation, generalized least-squares method and feasible generalized least-squares method. Hausman test and its limitations.

Assessment Elements

• Quiz 1
• Home assignment 1
• Quantitative research essay
• Seminar activity
• Quiz 2
• Home assignment 2

Interim Assessment

• Interim assessment (3 module)
  0.15 * Home assignment 1 + 0.15 * Home assignment 2 + 0.25 * Quantitative research essay + 0.15 * Quiz 1 + 0.15 * Quiz 2 + 0.15 * Seminar activity

Bibliography

Recommended Core Bibliography

• Charles N. Halaby. (2003). Running Head: Panel Models Panel Models in Sociological Research: Theory into Practice. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.72A0015F
• Анализ панельных данных и данных о длительности состояний : учеб. пособие, Ратникова, Т. А., 2014

Recommended Additional Bibliography

• Эконометрика. Начальный курс : учебник для вузов, Магнус, Я. Р., 2001