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Panel Data Analysis
for Comparative Research

Essex 2i

A survey of regression models for time series and time series cross-section data, with emphasis on modeling dynamics and panel structures. Participants will gain a basic understanding of the theory behind TSCS models and a working understanding of how to estimate, select, and interpret them, with a focus on the problems facing students of political economy, international relations, and comparative politics.

Essex 2i

Panel Data Analysis
for Comparative Research

Offered every Summer at the
University of Essex

For my University of  Washington
     course, click Panel Data again.


Summer 2015

Class meets:
MTWThF 2:15-5:45 pm
Teaching Center


Vilma Agalioti-Sgompou (Essex)

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


Supplementary material: Students looking to brush up on matrix algebra may want to read through Kevin Quinn's matrix algebra review in pdf format. In conjunction with this lecture I discuss examples that can be found in slides on labor standards in Africa and intimate partner homicide in the US.

Lab 1

Introduction to R


Supplementary material: This lab comes with three example scripts. First, there is an R code and data for exploratory data analysis using histograms and boxplots. Next, there is an R code and data for a simple bivariate linear regression. Finally, there is an R code and data for a multiple regression example.

Interested students can find detailed instructions for downloading, installing, and learning my recommended software for quantitative social science here. Focus on steps 1.1 and 1.3 for now, and then, optionally, step 1.2.

Topic 2

Time Series: Stochastic Processes


Supplementary material: 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

Models of Stationary Time Series


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

Topic 4

Models of Non-stationary Time Series


Supplementary material: 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


Supplementary material: 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


Supplementary material: 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


Supplementary material: 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


Supplementary material: 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

Course Problem Set

Problems may be turned in at any time during the course

Supplementary material: Data for problems 1, 3, and 4 in comma-separated variable format. Data for problem 4. Data for problem 5. Data for Bonus Problem A.

University of Washington link

CSSS Center for Statistics and the Social Sciences link

Chris Adolph & Erika Steiskal

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