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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 2024

Class meets:
MW 4:30–5:50 pm
Savery 131


Ramses Llobet
(UW Political Science)

Section meets:
F 1:30–3:30 pm
Section taught over Zoom

Lectures           Click on lecture titles to view slides or the buttons to download them as PDFs.

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. 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 via Canvas on Monday 15 April 2024

Data for problem 1. Data for problem 2. Data for problem 3.

Problem Set 2  

due via Canvas on Monday 6 May 2024

Data for problems 1 and 2.

Problem Set 3  

due via Canvas on Monday 20 May 2024

Data for problem 1.

Poster Presentations

22 to 29 May 2024

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

Final Paper

Due Tuesday 4 June 2024 at noon 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  

Supplementary material: You can access all the lab materials in the following .zip file, and to the lab session recording.

Lab 2

Time Series Diagnostics  

Supplementary material: After reviewing concepts, we will go voer the following .rmd file file which covers the necessary functions for time series diagnostics. Find in this .r file the code solutions for the practice exercise of identifying time trends. For the exercise, you will need this dataset, and to run the .rmd file this, and this datasets. You can access all the lab materials in the following .zip file, and to the lab session recording.

Lab 3

Modeling Stationary Time Series  

Supplementary material: After the presentation preview, we will go over the following .rmd file file, with all the lab contents for today. You will need the following dataset for the rmd file. This lab also largley replicates Chris's code for this module. You can access all the lab materials in the following .zip file, and to the lab session recording.

University of Washington link

CSSS Center for Statistics and the Social Sciences link

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Chris Adolph & Erika Steiskal

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