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Time Series and Panel Data for the Social Sciences


A survey of regression models for time series and time series cross-sectional data. Emphasis on modeling dynamics and panel structures with continuous outcomes, as well as on interpretation and fitting of models. Topics vary and may include trends and seasonality, ARIMA models, lagged dependent variables, distributed lags, cointegration and error correction models, fixed and random effects, panel heteroskedasticity, missing data imputation, and causal inference using panel data.


Time Series and Panel Data for the Social Sciences

Offered occasionally at the
University of Washington

For my Essex Summer School
     course, click Panel Data again.



Spring 2018

Class meets:
TTh 4:30–5:50 pm
Johnson Hall 175


Daniel Yoo
(UW Political Science)

Section meets:
F 1:00–2:20 pm
Savery Hall 117

Lectures           PDFs of slides are best viewed in Adobe Acrobat, rather than in your browser.

Topic 1

Course Introduction / Review of Linear Regression and Simulation    

Students should also review Topics 1 and 2 from my maximum likelihood course; I will answer questions about these lecture slides during the first week. Students looking to brush up on matrix algebra may want to read through Kevin Quinn's matrix algebra review in pdf format. Finally, in conjunction with these slides I discuss examples that can be found in slides on labor standards in Africa and intimate partner homicide in the US.

Topic 2

Basic Concepts for Time Series: Trends, Lags, and Cycles    

The univariate time series simulation function for R mentioned in the lecture is available here; this function allows for deterministic trends, stationary and nonstationary ARMA processes, and additive or multiplicative seasonality. Also available is a simpler but less flexible R script showing simulation and diagnostics of stationary processes using built-in functions.

Topic 3

Modeling Stationary Time Series    

Example code and csv data for estimating and interpreting ARMA models in R.

Topic 4

Modeling Nonstationary Time Series    

Example code for estimating and interpreting ARIMA and ECM models in R. You will also need this helper function for plotting counterfactual time series using R base graphics.

Topic 5

Basic Concepts for Panel Data    

For the curious, the R script used to construct the example plots in the first half of this lecture is here.

Topic 6

Panel Data Models with Many Time Periods    

Panel ARIMA and fixed effects models in R: example code and csv data for estimating and interpreting panel models with a large number of periods and a small number of cross-sectional units.

Topic 7

Panel Data Models with Few Time Periods    

Panel GMM models (Arellano-Bond/Blundell-Bond) in R: example code, helper functions, and csv data for estimating and interpreting panel models with a small number of periods and a large number of cross-sectional units. On Nickell bias: a script to plot the asymptotic results of Nickell (1981) as well as a helper file and two Monte Carlo scripts (large N / large β and large N / small β) to produce the finite sample results in the lecture slides.

Topic 8

Heteroskedasticity in Panel Data    

Topic 9

In-Sample Simulation for Panel Data Models  

Example code for simulating in-sample unit-by-unit from a panel data model. Uses the cigarette data and helper functions from Topic 7.

Advanced Topic 1

Missing Data and Multiple Imputation for Panel Data    

Advanced Topic 2

Panel Data with Binary Dependent Variables    

Student Assignments

Problem Set 1  

due in class Tuesday 17 April 2018

Data for problem 2. Data for problem 3.

Problem Set 2  

due in class Tuesday 1 May 2018

Data for problems 1 and 2.

Problem Set 3  

due in class Thursday 24 May 2018

Data for problem 1.

Poster Presentations

24 May 2018 to 31 May 2018

Requirements and suggestions for poster presentations will be discussed in class.

Final Paper

Due Tuesday 5 June 2018 at noon, both in my Gowen mailbox and by email

See the syllabus for paper requirements, and see my guidelines and recommendations for quantitative research papers.


Lab 1

Working with Time Series and Panel Data in R

Lab 1 slides and data.

Lab 2

Temporal Concepts: Trends, Stochastic Processes, and Seasonality

Lab 2 slides, data, and R script.

Lab 3

Modeling Stationary Time Series

Lab 3 slides, data and R script.

Lab 4

Modeling Nonstationary Time Series

Lab 4 slides, crude oil data, unemployment data, helper file, R script, and slides for cointegration analysis.

Lab 5

Panel Data Models with Many Periods

democracy data, helper file, simulation file, and R script.

Lab 6

Panel Data Models with Few Periods

Lab 6 slides, cigarette data, helper file, and R script.

University of Washington link

CSSS Center for Statistics and the Social Sciences link

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