Time Series Analysis for the Social Sciences

About

Workshop Contents and Objectives

Time series data lie at the core of political, economic, and social processes: public opinion shifts, government approval, macroeconomic indicators, media sentiment, policy changes, crises, and shocks are all phenomena that unfold over time. Yet most standard statistical tools assume independent observations and no temporal structure, assumptions that time-series data violate fundamentally. As a result, traditional regression approaches often produce misleading inferences, spurious findings, and incorrect conclusions, especially when series contain persistence, trends, unit roots, structural breaks, or long memory.

This course offers a systematic introduction to time series analysis tailored to social scientists. Using real political and economic applications, participants learn how to diagnose temporal dependence, model dynamic relationships, test for stationarity, work with integrated processes, estimate autoregressive distributed lag (ADL) and error-correction models (ECMs), and understand concepts such as cointegration, equation balance, fractional integration, and bounds testing.

The course draws on examples from political science, economics, and public opinion research to highlight why theory must align with the data-generating process, and why many published time-series analyses fail due to mis-specification. The course is hands-on and emphasizes both conceptual understanding and applied modeling skills. R and Stata (minimally) will be used throughout, with exercises designed to build reproducible workflows applicable to participants’ own research.

By the end of this course, participants will be able to:

Understand the properties of time series data and the implications for inference
Estimate and interpret ARIMA, ADL, ECM, and cointegration models
Diagnose stationarity, unit roots, and fractional integration
Apply equation balance tests and bounds approaches for inference
Design and implement complete empirical strategies for time-series-based research questions
Build reproducible analysis pipelines
Evaluate and compare dynamic models in applied social science research

Workshop design

Each day on the course will consist of two parts:

The morning lectures introducing key concepts, illustrated with political science and social-science applications, with live coding demonstrations.
The afternoon session will feature applied work involving guided exercises, replication tasks, and supervised project work, where participants can bring their own datasets and receive feedback on how to apply the methods.
During the course, participants are also welcome to work on their own projects. The instructor will happily assist with any individual project that requires skills and knowledge covered during the course.

All lecture slides, sample datasets, code scripts, and exercise solutions will be provided through an online repository. The course encourages constant interaction, group discussion, and hands-on engagement with data.

Detailed lecture plan (daily schedule)

Day 1 – Introduction and Foundations of Time Series Analysis
Morning:

Time series notation and terminology
Autocorrelation, autoregression, serial dependence
Stationarity and weak dependence
Trends, cycles, structural breaks
Integration and instability
Afternoon:

Hands-on exploration of time series data in R
Descriptive analysis using ACF/PACF
Checking for stationarity and structural breaks
Exercise: students identify a series from their own research and run diagnostic plots
Day 2 – Dynamic Models, Equation Balance, ADL & ECM
Morning:

Time-series regression assumptions
Dynamic regression models and lag structure
The ADL model
The relationship between ADL model and ECM
Equation balance
General-to-specific model building
Afternoon:

Estimating ADL models, lag selection, diagnostics
Computing LRMs and interpreting dynamic effects
Exercise: students estimate an ADL model using their own series, check equation balance, and produce preliminary diagnostics
Day 3 – Cointegration & Error Correction
Morning:

Cointegration and equilibrium relationships
The Engle-Granger two-step approach
One-Step, GECM
Inference and Interpretation from GECM
Afternoon:

Step-by-step Engle–Granger implementation in software
Determining cointegrating vectors
Testing for cointegration in sample datasets
Exercise: students identify whether pairs of their variables cointegrate; interpret cointegrating equilibrium
Day 4 – Fractional Integration & Advanced ECM/GECM Inference
Morning:

Fractional integration and ARFIMA models
I(1) vs FI processes
Fractional cointegration
GECM inference pitfalls
Afternoon:

Estimating ARFIMA and interpreting memory
Applying FI ideas to pre-whitening
Fractional cointegration practical
Exercise: students compute ACFs, test for FI, and compare ARIMA vs ARFIMA fits with their own project data
Day 5 – Bounds Approaches & Full Time-Series Research Designs
Morning:

Pesaran–Shin–Smith bounds testing (ARDL bounds)
Long-run multiplier bounds
Interpreting bounds results for theory-driven social-science questions
Building full empirical pipelines: diagnostics, specification and interpretation
Afternoon:

Guided estimation of bounds models in R
Student mini-presentations: My time series design in 3 slides

Course feedback and Q&A.

Class materials

All materials will be provided online

Course leader

Chendi Wang is an Assistant Professor of Political Science at the Department of Political Science and Public Administration, Vrije Universiteit Amsterdam. He earned his Ph.D. in Political Science from the European University Institute in 2021. His research spans comparative politics, political behaviour, political economy, and political methodology, with current work on European politics, comparative political economy of crisis and macro-policy, party and electoral politics, and political mobilisation

Target group

primarily graduate researchers, PhD researchers, early career researchers

**The Summer School cannot grant credits. We only deliver a Certificate of Participation, i.e. we certify your attendance.**

If you consider using Summer School workshops to obtain credits (ECTS), you will have to investigate at your home institution (contact the person/institute responsible for your degree) to find out whether they recognise the Summer School, how many credits can be earned from a workshop/course with roughly 35 hours of teaching, no graded work, and no exams.

Make sure to investigate this matter before registering if this is important to you.

Prerequisites

Students who intend to enroll in this course are required to have basic knowledge in statistical analysis including linear regression models and hypothesis testing. Participants are expected to have basic computer and statistical analysis skills. Basic familiarity with R is necessary to participate in practical exercises and activities

Fee info